Relational and XML Data Exchange
Data exchange is the problem of finding an instance of a target schema, given an instance of a source schema and a specification of the relationship between the source and the target. Such a target instance should correctly represent information from the source instance under the constraints imposed by the target schema, and it should allow one to evaluate queries on the target instance in a way that is semantically consistent with the source data. Data exchange is an old problem that re-emerged as an active research topic recently, due to the increased need for exchange of data in various formats, often in e-business applications. In this lecture, we give an overview of the basic concepts of data exchange in both relational and XML contexts. We give examples of data exchange problems, and we introduce the main tasks that need to addressed. We then discuss relational data exchange, concentrating on issues such as relational schema mappings, materializing target instances (including canonical solutions and cores), query answering, and query rewriting. After that, we discuss metadata management, i.e., handling schema mappings themselves. We pay particular attention to operations on schema mappings, such as composition and inverse. Finally, we describe both data exchange and metadata management in the context of XML. We use mappings based on transforming tree patterns, and we show that they lead to a host of new problems that did not arise in the relational case, but they need to be addressed for XML. These include consistency issues for mappings and schemas, as well as imposing tighter restrictions on mappings and queries to achieve tractable query answering in data exchange. Table of Contents: Overview / Relational Mappings and Data Exchange / Metadata Management / XML Mappings and Data Exchange
Data Stream Management
Many applications process high volumes of streaming data, among them Internet traffic analysis, financial tickers, and transaction log mining. In general, a data stream is an unbounded data set that is produced incrementally over time, rather than being available in full before its processing begins. In this lecture, we give an overview of recent research in stream processing, ranging from answering simple queries on high-speed streams to loading real-time data feeds into a streaming warehouse for off-line analysis. We will discuss two types of systems for end-to-end stream processing: Data Stream Management Systems (DSMSs) and Streaming Data Warehouses (SDWs). A traditional database management system typically processes a stream of ad-hoc queries over relatively static data. In contrast, a DSMS evaluates static (long-running) queries on streaming data, making a single pass over the data and using limited working memory. In the first part of this lecture, we will discuss research problems in DSMSs, such as continuous query languages, non-blocking query operators that continually react to new data, and continuous query optimization. The second part covers SDWs, which combine the real-time response of a DSMS by loading new data as soon as they arrive with a data warehouse's ability to manage Terabytes of historical data on secondary storage. Table of Contents: Introduction / Data Stream Management Systems / Streaming Data Warehouses / Conclusions
Latent Semantic Mapping
Latent semantic mapping (LSM) is a generalization of latent semantic analysis (LSA), a paradigm originally developed to capture hidden word patterns in a text document corpus. In information retrieval, LSA enables retrieval on the basis of conceptual content, instead of merely matching words between queries and documents. It operates under the assumption that there is some latent semantic structure in the data, which is partially obscured by the randomness of word choice with respect to retrieval. Algebraic and/or statistical techniques are brought to bear to estimate this structure and get rid of the obscuring ""noise."" This results in a parsimonious continuous parameter description of words and documents, which then replaces the original parameterization in indexing and retrieval. This approach exhibits three main characteristics: -Discrete entities (words and documents) are mapped onto a continuous vector space; -This mapping is determined by global correlation patterns; and -Dimensionality reduction is an integral part of the process. Such fairly generic properties are advantageous in a variety of different contexts, which motivates a broader interpretation of the underlying paradigm. The outcome (LSM) is a data-driven framework for modeling meaningful global relationships implicit in large volumes of (not necessarily textual) data. This monograph gives a general overview of the framework, and underscores the multifaceted benefits it can bring to a number of problems in natural language understanding and spoken language processing. It concludes with a discussion of the inherent tradeoffs associated with the approach, and some perspectives on its general applicability to data-driven information extraction. Contents: I. Principles / Introduction / Latent Semantic Mapping / LSM Feature Space / Computational Effort / Probabilistic Extensions / II. Applications/ Junk E-mail Filtering / Semantic Classification / Language Modeling / Pronunciation Modeling / Speaker Verification / TTS Unit Selection / III. Perspectives / Discussion / Conclusion / Bibliography
Articulation and Intelligibility
Immediately following the Second World War, between 1947 and 1955, several classic papers quantified the fundamentals of human speech information processing and recognition. In 1947 French and Steinberg published their classic study on the articulation index. In 1948 Claude Shannon published his famous work on the theory of information. In 1950 Fletcher and Galt published their theory of the articulation index, a theory that Fletcher had worked on for 30 years, which integrated his classic works on loudness and speech perception with models of speech intelligibility. In 1951 George Miller then wrote the first book Language and Communication, analyzing human speech communication with Claude Shannon's just published theory of information. Finally in 1955 George Miller published the first extensive analysis of phone decoding, in the form of confusion matrices, as a function of the speech-to-noise ratio. This work extended the Bell Labs' speech articulation studies with ideas from Shannon's Information theory. Both Miller and Fletcher showed that speech, as a code, is incredibly robust to mangling distortions of filtering and noise. Regrettably much of this early work was forgotten. While the key science of information theory blossomed, other than the work of George Miller, it was rarely applied to aural speech research. The robustness of speech, which is the most amazing thing about the speech code, has rarely been studied. It is my belief (i.e., assumption) that we can analyze speech intelligibility with the scientific method. The quantitative analysis of speech intelligibility requires both science and art. The scientific component requires an error analysis of spoken communication, which depends critically on the use of statistics, information theory, and psychophysical methods. The artistic component depends on knowing how to restrict the problem in such a way that progress may be made. It is critical to tease out the relevant from the irrelevant and dig for the key issues. This will focus us on the decoding of nonsense phonemes with no visual component, which have been mangled by filtering and noise. This monograph is a summary and theory of human speech recognition. It builds on and integrates the work of Fletcher, Miller, and Shannon. The long-term goal is to develop a quantitative theory for predicting the recognition of speech sounds. In Chapter 2 the theory is developed for maximum entropy (MaxEnt) speech sounds, also called nonsense speech. In Chapter 3, context is factored in. The book is largely reflective, and quantitative, with a secondary goal of providing an historical context, along with the many deep insights found in these early works.
Uncertain Schema Matching
Schema matching is the task of providing correspondences between concepts describing the meaning of data in various heterogeneous, distributed data sources. Schema matching is one of the basic operations required by the process of data and schema integration, and thus has a great effect on its outcomes, whether these involve targeted content delivery, view integration, database integration, query rewriting over heterogeneous sources, duplicate data elimination, or automatic streamlining of workflow activities that involve heterogeneous data sources. Although schema matching research has been ongoing for over 25 years, more recently a realization has emerged that schema matchers are inherently uncertain. Since 2003, work on the uncertainty in schema matching has picked up, along with research on uncertainty in other areas of data management. This lecture presents various aspects of uncertainty in schema matching within a single unified framework. We introduce basic formulations of uncertainty and provide several alternative representations of schema matching uncertainty. Then, we cover two common methods that have been proposed to deal with uncertainty in schema matching, namely ensembles, and top-K matchings, and analyze them in this context. We conclude with a set of real-world applications. Table of Contents: Introduction / Models of Uncertainty / Modeling Uncertain Schema Matching / Schema Matcher Ensembles / Top-K Schema Matchings / Applications / Conclusions and Future Work
Natural Language Processing for Historical Texts
More and more historical texts are becoming available in digital form. Digitization of paper documents is motivated by the aim of preserving cultural heritage and making it more accessible, both to laypeople and scholars. As digital images cannot be searched for text, digitization projects increasingly strive to create digital text, which can be searched and otherwise automatically processed, in addition to facsimiles. Indeed, the emerging field of digital humanities heavily relies on the availability of digital text for its studies. Together with the increasing availability of historical texts in digital form, there is a growing interest in applying natural language processing (NLP) methods and tools to historical texts. However, the specific linguistic properties of historical texts -- the lack of standardized orthography, in particular -- pose special challenges for NLP. This book aims to give an introduction to NLP for historical texts and an overview of the state of the art in this field. The book starts with an overview of methods for the acquisition of historical texts (scanning and OCR), discusses text encoding and annotation schemes, and presents examples of corpora of historical texts in a variety of languages. The book then discusses specific methods, such as creating part-of-speech taggers for historical languages or handling spelling variation. A final chapter analyzes the relationship between NLP and the digital humanities. Certain recently emerging textual genres, such as SMS, social media, and chat messages, or newsgroup and forum postings share a number of properties with historical texts, for example, nonstandard orthography and grammar, and profuse use of abbreviations. The methods and techniques required for the effective processing of historical texts are thus also of interest for research in other domains. Table of Contents: Introduction / NLP and Digital Humanities / Spelling in Historical Texts / Acquiring Historical Texts / Text Encoding andAnnotation Schemes / Handling Spelling Variation / NLP Tools for Historical Languages / Historical Corpora / Conclusion / Bibliography
User-Centered Data Management
This lecture covers several core issues in user-centered data management, including how to design usable interfaces that suitably support database tasks, and relevant approaches to visual querying, information visualization, and visual data mining. Novel interaction paradigms, e.g., mobile and interfaces that go beyond the visual dimension, are also discussed. Table of Contents: Why User-Centered / The Early Days: Visual Query Systems / Beyond Querying / More Advanced Applications / Non-Visual Interfaces / Conclusions
Control Systems Synthesis
This book introduces the so-called ""stable factorization approach"" to the synthesis of feedback controllers for linear control systems. The key to this approach is to view the multi-input, multi-output (MIMO) plant for which one wishes to design a controller as a matrix over the fraction field F associated with a commutative ring with identity, denoted by R, which also has no divisors of zero. In this setting, the set of single-input, single-output (SISO) stable control systems is precisely the ring R, while the set of stable MIMO control systems is the set of matrices whose elements all belong to R. The set of unstable, meaning not necessarily stable, control systems is then taken to be the field of fractions F associated with R in the SISO case, and the set of matrices with elements in F in the MIMO case. The central notion introduced in the book is that, in most situations of practical interest, every matrix P whose elements belong to F can be ""factored"" as a ""ratio"" of two matrices N, D whose elements belong to R, in such a way that N, D are coprime. In the familiar case where the ring R corresponds to the set of bounded-input, bounded-output (BIBO)-stable rational transfer functions, coprimeness is equivalent to two functions not having any common zeros in the closed right half-plane including infinity. However, the notion of coprimeness extends readily to discrete-time systems, distributed-parameter systems in both the continuous- as well as discrete-time domains, and to multi-dimensional systems. Thus the stable factorization approach enables one to capture all these situations within a common framework. The key result in the stable factorization approach is the parametrization of all controllers that stabilize a given plant. It is shown that the set of all stabilizing controllers can be parametrized by a single parameter R, whose elements all belong to R. Moreover, every transfer matrix in the closed-loop system is an affine function of the design parameter R. Thus problems of reliable stabilization, disturbance rejection, robust stabilization etc. can all be formulated in terms of choosing an appropriate R. This is a reprint of the book Control System Synthesis: A Factorization Approach originally published by M.I.T. Press in 1985. Table of Contents: Introduction / Proper Stable Rational Functions / Scalar Systems: An Introduction / Matrix Rings / Stabilization
Mining Structures of Factual Knowledge from Text
The real-world data, though massive, is largely unstructured, in the form of natural-language text. It is challenging but highly desirable to mine structures from massive text data, without extensive human annotation and labeling. In this book, we investigate the principles and methodologies of mining structures of factual knowledge (e.g., entities and their relationships) from massive, unstructured text corpora. Departing from many existing structure extraction methods that have heavy reliance on human annotated data for model training, our effort-light approach leverages human-curated facts stored in external knowledge bases as distant supervision and exploits rich data redundancy in large text corpora for context understanding. This effort-light mining approach leads to a series of new principles and powerful methodologies for structuring text corpora, including (1) entity recognition, typing and synonym discovery, (2) entity relation extraction, and (3) open-domain attribute-valuemining and information extraction. This book introduces this new research frontier and points out some promising research directions.
Control Systems Synthesis
This book introduces the so-called ""stable factorization approach"" to the synthesis of feedback controllers for linear control systems. The key to this approach is to view the multi-input, multi-output (MIMO) plant for which one wishes to design a controller as a matrix over the fraction field F associated with a commutative ring with identity, denoted by R, which also has no divisors of zero. In this setting, the set of single-input, single-output (SISO) stable control systems is precisely the ring R, while the set of stable MIMO control systems is the set of matrices whose elements all belong to R. The set of unstable, meaning not necessarily stable, control systems is then taken to be the field of fractions F associated with R in the SISO case, and the set of matrices with elements in F in the MIMO case. The central notion introduced in the book is that, in most situations of practical interest, every matrix P whose elements belong to F can be ""factored"" as a ""ratio"" of two matrices N, D whose elements belong to R, in such a way that N, D are coprime. In the familiar case where the ring R corresponds to the set of bounded-input, bounded-output (BIBO)-stable rational transfer functions, coprimeness is equivalent to two functions not having any common zeros in the closed right half-plane including infinity. However, the notion of coprimeness extends readily to discrete-time systems, distributed-parameter systems in both the continuous- as well as discrete-time domains, and to multi-dimensional systems. Thus the stable factorization approach enables one to capture all these situations within a common framework. The key result in the stable factorization approach is the parametrization of all controllers that stabilize a given plant. It is shown that the set of all stabilizing controllers can be parametrized by a single parameter R, whose elements all belong to R. Moreover, every transfer matrix in the closed-loop system is an affine function of the design parameter R. Thus problems of reliable stabilization, disturbance rejection, robust stabilization etc. can all be formulated in terms of choosing an appropriate R. This is a reprint of the book Control System Synthesis: A Factorization Approach originally published by M.I.T. Press in 1985. Table of Contents: Filtering and Sensitivity Minimization / Robustness / Extensions to General Settings
Scalable Processing of Spatial-Keyword Queries
Text data that is associated with location data has become ubiquitous. A tweet is an example of this type of data, where the text in a tweet is associated with the location where the tweet has been issued. We use the term spatial-keyword data to refer to this type of data. Spatial-keyword data is being generated at massive scale. Almost all online transactions have an associated spatial trace. The spatial trace is derived from GPS coordinates, IP addresses, or cell-phone-tower locations. Hundreds of millions or even billions of spatial-keyword objects are being generated daily. Spatial-keyword data has numerous applications that require efficient processing and management of massive amounts of spatial-keyword data. This book starts by overviewing some important applications of spatial-keyword data, and demonstrates the scale at which spatial-keyword data is being generated. Then, it formalizes and classifies the various types of queries that execute over spatial-keyword data.Next, it discusses important and desirable properties of spatial-keyword query languages that are needed to express queries over spatial-keyword data. As will be illustrated, existing spatial-keyword query languages vary in the types of spatial-keyword queries that they can support. There are many systems that process spatial-keyword queries. Systems differ from each other in various aspects, e.g., whether the system is batch-oriented or stream-based, and whether the system is centralized or distributed. Moreover, spatial-keyword systems vary in the types of queries that they support. Finally, systems vary in the types of indexing techniques that they adopt. This book provides an overview of the main spatial-keyword data-management systems (SKDMSs), and classifies them according to their features. Moreover, the book describes the main approaches adopted when indexing spatial-keyword data in the centralized and distributed settings. Several case studies of {SKDMSs} are presentedalong with the applications and query types that these {SKDMSs} are targeted for and the indexing techniques they utilize for processing their queries. Optimizing the performance and the query processing of {SKDMSs} still has many research challenges and open problems. The book concludes with a discussion about several important and open research-problems in the domain of scalable spatial-keyword processing.
Exploratory Causal Analysis with Time Series Data
Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments. Data analysis techniques are required for identifying causal information and relationships directly from such observational data. This need has led to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a framework for exploring potential causal structures in time series data sets and is characterized by a myopic goal to determine which data series from a given set of series might be seen as the primary driver. In this work, ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise.
Non-Volatile Memory Database Management Systems
This book explores the implications of non-volatile memory (NVM) for database management systems (DBMSs). The advent of NVM will fundamentally change the dichotomy between volatile memory and durable storage in DBMSs. These new NVM devices are almost as fast as volatile memory, but all writes to them are persistent even after power loss. Existing DBMSs are unable to take full advantage of this technology because their internal architectures are predicated on the assumption that memory is volatile. With NVM, many of the components of legacy DBMSs are unnecessary and will degrade the performance of data-intensive applications. We present the design and implementation of DBMS architectures that are explicitly tailored for NVM. The book focuses on three aspects of a DBMS: (1) logging and recovery, (2) storage and buffer management, and (3) indexing. First, we present a logging and recovery protocol that enables the DBMS to support near-instantaneous recovery. Second, we propose astorage engine architecture and buffer management policy that leverages the durability and byte-addressability properties of NVM to reduce data duplication and data migration. Third, the book presents the design of a range index tailored for NVM that is latch-free yet simple to implement. All together, the work described in this book illustrates that rethinking the fundamental algorithms and data structures employed in a DBMS for NVM improves performance and availability, reduces operational cost, and simplifies software development.
Exploiting the Power of Group Differences
This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included. Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on. EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines. Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest. We believe that many effective ways to exploit group differences' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.
Natural Language Data Management and Interfaces
The volume of natural language text data has been rapidly increasing over the past two decades, due to factors such as the growth of the Web, the low cost associated with publishing, and the progress on the digitization of printed texts. This growth combined with the proliferation of natural language systems for search and retrieving information provides tremendous opportunities for studying some of the areas where database systems and natural language processing systems overlap. This book explores two interrelated and important areas of overlap: (1) managing natural language data and (2) developing natural language interfaces to databases. It presents relevant concepts and research questions, state-of-the-art methods, related systems, and research opportunities and challenges covering both areas. Relevant topics discussed on natural language data management include data models, data sources, queries, storage and indexing, and transforming natural language text. Under naturallanguage interfaces, it presents the anatomy of these interfaces to databases, the challenges related to query understanding and query translation, and relevant aspects of user interactions. Each of the challenges is covered in a systematic way: first starting with a quick overview of the topics, followed by a comprehensive view of recent techniques that have been proposed to address the challenge along with illustrative examples. It also reviews some notable systems in details in terms of how they address different challenges and their contributions. Finally, it discusses open challenges and opportunities for natural language management and interfaces. The goal of this book is to provide an introduction to the methods, problems, and solutions that are used in managing natural language data and building natural language interfaces to databases. It serves as a starting point for readers who are interested in pursuing additional work on these exciting topics in both academic andindustrial environments.
Mining Latent Entity Structures
The "big data" era is characterized by an explosion of information in the form of digital data collections, ranging from scientific knowledge, to social media, news, and everyone's daily life. Examples of such collections include scientific publications, enterprise logs, news articles, social media, and general web pages. Valuable knowledge about multi-typed entities is often hidden in the unstructured or loosely structured, interconnected data. Mining latent structures around entities uncovers hidden knowledge such as implicit topics, phrases, entity roles and relationships. In this monograph, we investigate the principles and methodologies of mining latent entity structures from massive unstructured and interconnected data. We propose a text-rich information network model for modeling data in many different domains. This leads to a series of new principles and powerful methodologies for mining latent structures, including (1) latent topical hierarchy, (2) quality topical phrases, (3)entity roles in hierarchical topical communities, and (4) entity relations. This book also introduces applications enabled by the mined structures and points out some promising research directions.
Individual and Collective Graph Mining
Graphs naturally represent information ranging from links between web pages, to communication in email networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect anomalies that indicate critical events, such as an attack on a computer system, disease formation in the human brain, or the fall of a company? This book presents scalable, principled discovery algorithms that combine globality with locality to make sense of one or more graphs. In addition to fast algorithmic methodologies, we also contribute graph-theoretical ideas and models, and real-world applications in two main areas: Individual Graph Mining: We show how to interpretably summarize a single graph by identifying its important graph structures. We complement summarization with inference, which leverages information about few entities (obtained via summarization or other methods) and the network structure to efficiently and effectively learn information about the unknown entities. Collective Graph Mining: We extend the idea of individual-graph summarization to time-evolving graphs, and show how to scalably discover temporal patterns. Apart from summarization, we claim that graph similarity is often the underlying problem in a host of applications where multiple graphs occur (e.g., temporal anomaly detection, discovery of behavioral patterns), and we present principled, scalable algorithms for aligning networks and measuring their similarity. The methods that we present in this book leverage techniques from diverse areas, such as matrix algebra, graph theory, optimization, information theory, machine learning, finance, and social science, to solve real-world problems. We present applications of our exploration algorithms to massive datasets, including a Web graph of 6.6 billion edges, a Twitter graph of 1.8 billion edges, brain graphs with up to 90 million edges, collaboration, peer-to-peer networks, browser logs, all spanning millions of users and interactions.
Data Management in Machine Learning Systems
Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scalable manner. Data management is at the heart of many ML systems due to data-driven application characteristics, data-centric workload characteristics, and system architectures inspired by classical data management techniques. In this book, we follow this data-centric view of ML systems and aim to provide a comprehensive overview of data management in ML systems for the end-to-end data science or ML lifecycle. We review multiple interconnected lines of work: (1) ML support in database (DB) systems, (2) DB-inspired ML systems, and (3) ML lifecycle systems. Covered topics include: in-database analytics via query generation and user-defined functions, factorized and statistical-relational learning; optimizing compilers for ML workloads; execution strategies and hardware accelerators;data access methods such as compression, partitioning and indexing; resource elasticity and cloud markets; as well as systems for data preparation for ML, model selection, model management, model debugging, and model serving. Given the rapidly evolving field, we strive for a balance between an up-to-date survey of ML systems, an overview of the underlying concepts and techniques, as well as pointers to open research questions. Hence, this book might serve as a starting point for both systems researchers and developers.
Skylines and Other Dominance-Based Queries
This book is a gentle introduction to dominance-based query processing techniques and their applications. The book aims to present fundamental as well as some advanced issues in the area in a precise, but easy-to-follow, manner. Dominance is an intuitive concept that can be used in many different ways in diverse application domains. The concept of dominance is based on the values of the attributes of each object. An object ���� dominates another object ���� if ���� is better than ����. This goodness criterion may differ from one user to another. However, all decisions boil down to the minimization or maximization of attribute values. In this book, we will explore algorithms and applications related to dominance-based query processing. The concept of dominance has a long history in finance and multi-criteria optimization. However, the introduction of the concept to the database community in 2001 inspired many researchers to contribute to the area. Therefore, many algorithmic techniqueshave been proposed for the efficient processing of dominance-based queries, such as skyline queries, ����-dominant queries, and top-���� dominating queries, just to name a few.
On Transactional Concurrency Control
This book contains a number of chapters on transactional database concurrency control. This volume's entire sequence of chapters can summarized as follows: A two-sentence summary of the volume's entire sequence of chapters is this: traditional locking techniques can be improved in multiple dimensions, notably in lock scopes (sizes), lock modes (increment, decrement, and more), lock durations (late acquisition, early release), and lock acquisition sequence (to avoid deadlocks). Even if some of these improvements can be transferred to optimistic concurrency control, notably a fine granularity of concurrency control with serializable transaction isolation including phantom protection, pessimistic concurrency control is categorically superior to optimistic concurrency control, i.e., independent of application, workload, deployment, hardware, and software implementation.
Detecting Fake News on Social Media
In the past decade, social media has become increasingly popular for news consumption due to its easy access, fast dissemination, and low cost. However, social media also enables the wide propagation of "fake news," i.e., news with intentionally false information. Fake news on social media can have significant negative societal effects. Therefore, fake news detection on social media has recently become an emerging research area that is attracting tremendous attention. This book, from a data mining perspective, introduces the basic concepts and characteristics of fake news across disciplines, reviews representative fake news detection methods in a principled way, and illustrates challenging issues of fake news detection on social media. In particular, we discussed the value of news content and social context, and important extensions to handle early detection, weakly-supervised detection, and explainable detection. The concepts, algorithms, and methods described in this lecture can help harness the power of social media to build effective and intelligent fake news detection systems. This book is an accessible introduction to the study of detecting fake news on social media. It is an essential reading for students, researchers, and practitioners to understand, manage, and excel in this area. This book is supported by additional materials, including lecture slides, the complete set of figures, key references, datasets, tools used in this book, and the source code of representative algorithms. The readers are encouraged to visit the book website for the latest information: http: //dmml.asu.edu/dfn/
Datalog and Logic Databases
The use of logic in databases started in the late 1960s. In the early 1970s Codd formalized databases in terms of the relational calculus and the relational algebra. A major influence on the use of logic in databases was the development of the field of logic programming. Logic provides a convenient formalism for studying classical database problems and has the important property of being declarative, that is, it allows one to express what she wants rather than how to get it. For a long time, relational calculus and algebra were considered the relational database languages. However, there are simple operations, such as computing the transitive closure of a graph, which cannot be expressed with these languages. Datalog is a declarative query language for relational databases based on the logic programming paradigm. One of the peculiarities that distinguishes Datalog from query languages like relational algebra and calculus is recursion, which gives Datalog the capability to express queries like computing a graph transitive closure. Recent years have witnessed a revival of interest in Datalog in a variety of emerging application domains such as data integration, information extraction, networking, program analysis, security, cloud computing, ontology reasoning, and many others. The aim of this book is to present the basics of Datalog, some of its extensions, and recent applications to different domains.
Data Exploration Using Example-Based Methods
Data usually comes in a plethora of formats and dimensions, rendering the exploration and information extraction processes challenging. Thus, being able to perform exploratory analyses in the data with the intent of having an immediate glimpse on some of the data properties is becoming crucial. Exploratory analyses should be simple enough to avoid complicate declarative languages (such as SQL) and mechanisms, and at the same time retain the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user, or the analyst, circumvents query languages by using examples as input. An example is a representative of the intended results, or in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind, but may not able to (easily) express. They can be useful in cases where a user is looking for information in an unfamiliar dataset, when the task is particularly challenging like finding duplicate items, or simply when they are exploring the data. In this book, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how that different data types require different techniques, and present algorithms that are specifically designed for relational, textual, and graph data. The book presents also the challenges and the new frontiers of machine learning in online settings which recently attracted the attention of the database community. The lecture concludes with a vision for further research and applications in this area.
Transaction Processing on Modern Hardware
The last decade has brought groundbreaking developments in transaction processing. This resurgence of an otherwise mature research area has spurred from the diminishing cost per GB of DRAM that allows many transaction processing workloads to be entirely memory-resident. This shift demanded a pause to fundamentally rethink the architecture of database systems. The data storage lexicon has now expanded beyond spinning disks and RAID levels to include the cache hierarchy, memory consistency models, cache coherence and write invalidation costs, NUMA regions, and coherence domains. New memory technologies promise fast non-volatile storage and expose unchartered trade-offs for transactional durability, such as exploiting byte-addressable hot and cold storage through persistent programming that promotes simpler recovery protocols. In the meantime, the plateauing single-threaded processor performance has brought massive concurrency within a single node, first in the form of multi-core, andnow with many-core and heterogeneous processors. The exciting possibility to reshape the storage, transaction, logging, and recovery layers of next-generation systems on emerging hardware have prompted the database research community to vigorously debate the trade-offs between specialized kernels that narrowly focus on transaction processing performance vs. designs that permit transactionally consistent data accesses from decision support and analytical workloads. In this book, we aim to classify and distill the new body of work on transaction processing that has surfaced in the last decade to navigate researchers and practitioners through this intricate research subject.
Full-Text (Substring) Indexes in External Memory
Nowadays, textual databases are among the most rapidly growing collections of data. Some of these collections contain a new type of data that differs from classical numerical or textual data. These are long sequences of symbols, not divided into well-separated small tokens (words). The most prominent among such collections are databases of biological sequences, which are experiencing today an unprecedented growth rate. Starting in 2008, the "1000 Genomes Project" has been launched with the ultimate goal of collecting sequences of additional 1,500 Human genomes, 500 each of European, African, and East Asian origin. This will produce an extensive catalog of Human genetic variations. The size of just the raw sequences in this catalog would be about 5 terabytes. Querying strings without well-separated tokens poses a different set of challenges, typically addressed by building full-text indexes, which provide effective structures to index all the substrings of the given strings. Since full-text indexes occupy more space than the raw data, it is often necessary to use disk space for their construction. However, until recently, the construction of full-text indexes in secondary storage was considered impractical due to excessive I/O costs. Despite this, algorithms developed in the last decade demonstrated that efficient external construction of full-text indexes is indeed possible. This book is about large-scale construction and usage of full-text indexes. We focus mainly on suffix trees, and show efficient algorithms that can convert suffix trees to other kinds of full-text indexes and vice versa. There are four parts in this book. They are a mix of string searching theory with the reality of external memory constraints. The first part introduces general concepts of full-text indexes and shows the relationships between them. The second part presents the first series of external-memory construction algorithms that can handle the construction of full-text indexes for moderately large strings in the order of few gigabytes. The third part presents algorithms that scale for very large strings. The final part examines queries that can be facilitated by disk-resident full-text indexes. Table of Contents: Structures for Indexing Substrings / External Construction of Suffix Trees / Scaling Up: When the Input Exceeds the Main Memory / Queries for Disk-based Indexes / Conclusions and Open Problems
Mining Human Mobility in Location-Based Social Networks
In recent years, there has been a rapid growth of location-based social networking services, such as Foursquare and Facebook Places, which have attracted an increasing number of users and greatly enriched their urban experience. Typical location-based social networking sites allow a user to "check in" at a real-world POI (point of interest, e.g., a hotel, restaurant, theater, etc.), leave tips toward the POI, and share the check-in with their online friends. The check-in action bridges the gap between real world and online social networks, resulting in a new type of social networks, namely location-based social networks (LBSNs). Compared to traditional GPS data, location-based social networks data contains unique properties with abundant heterogeneous information to reveal human mobility, i.e., "when and where a user (who) has been to for what," corresponding to an unprecedented opportunity to better understand human mobility from spatial, temporal, social, and content aspects. The mining and understanding of human mobility can further lead to effective approaches to improve current location-based services from mobile marketing to recommender systems, providing users more convenient life experience than before. This book takes a data mining perspective to offer an overview of studying human mobility in location-based social networks and illuminate a wide range of related computational tasks. It introduces basic concepts, elaborates associated challenges, reviews state-of-the-art algorithms with illustrative examples and real-world LBSN datasets, and discusses effective evaluation methods in mining human mobility. In particular, we illustrate unique characteristics and research opportunities of LBSN data, present representative tasks of mining human mobility on location-based social networks, including capturing user mobility patterns to understand when and where a user commonly goes (location prediction), and exploiting user preferences and location profiles to investigate where and when a user wants to explore (location recommendation), along with studying a user's check-in activity in terms of why a user goes to a certain location.
Data Processing on FPGAs
Roughly a decade ago, power consumption and heat dissipation concerns forced the semiconductor industry to radically change its course, shifting from sequential to parallel computing. Unfortunately, improving performance of applications has now become much more difficult than in the good old days of frequency scaling. This is also affecting databases and data processing applications in general, and has led to the popularity of so-called data appliances--specialized data processing engines, where software and hardware are sold together in a closed box. Field-programmable gate arrays (FPGAs) increasingly play an important role in such systems. FPGAs are attractive because the performance gains of specialized hardware can be significant, while power consumption is much less than that of commodity processors. On the other hand, FPGAs are way more flexible than hard-wired circuits (ASICs) and can be integrated into complex systems in many different ways, e.g., directly in the network for a high-frequency trading application. This book gives an introduction to FPGA technology targeted at a database audience. In the first few chapters, we explain in detail the inner workings of FPGAs. Then we discuss techniques and design patterns that help mapping algorithms to FPGA hardware so that the inherent parallelism of these devices can be leveraged in an optimal way. Finally, the book will illustrate a number of concrete examples that exploit different advantages of FPGAs for data processing. Table of Contents: Preface / Introduction / A Primer in Hardware Design / FPGAs / FPGA Programming Models / Data Stream Processing / Accelerated DB Operators / Secure Data Processing / Conclusions / Bibliography / Authors' Biographies / Index
Data Protection from Insider Threats
As data represent a key asset for today's organizations, the problem of how to protect this data from theft and misuse is at the forefront of these organizations' minds. Even though today several data security techniques are available to protect data and computing infrastructures, many such techniques -- such as firewalls and network security tools -- are unable to protect data from attacks posed by those working on an organization's "inside." These "insiders" usually have authorized access to relevant information systems, making it extremely challenging to block the misuse of information while still allowing them to do their jobs. This book discusses several techniques that can provide effective protection against attacks posed by people working on the inside of an organization. Chapter One introduces the notion of insider threat and reports some data about data breaches due to insider threats. Chapter Two covers authentication and access control techniques, and Chapter Three shows how these general security techniques can be extended and used in the context of protection from insider threats. Chapter Four addresses anomaly detection techniques that are used to determine anomalies in data accesses by insiders. These anomalies are often indicative of potential insider data attacks and therefore play an important role in protection from these attacks. Security information and event management (SIEM) tools and fine-grained auditing are discussed in Chapter Five. These tools aim at collecting, analyzing, and correlating -- in real-time -- any information and event that may be relevant for the security of an organization. As such, they can be a key element in finding a solution to such undesirable insider threats. Chapter Six goes on to provide a survey of techniques for separation-of-duty (SoD). SoD is an important principle that, when implemented in systems and tools, can strengthen data protection from malicious insiders. However, to date, very few approaches have been proposed for implementing SoD in systems. In Chapter Seven, a short survey of a commercial product is presented, which provides different techniques for protection from malicious users with system privileges -- such as a DBA in database management systems. Finally, in Chapter Eight, the book concludes with a few remarks and additional research directions. Table of Contents: Introduction / Authentication / Access Control / Anomaly Detection / Security Information and Event Management and Auditing / Separation of Duty / Case Study: Oracle Database Vault / Conclusion
Perspectives on Business Intelligence
In the 1980s, traditional Business Intelligence (BI) systems focused on the delivery of reports that describe the state of business activities in the past, such as for questions like "How did our sales perform during the last quarter?" A decade later, there was a shift to more interactive content that presented how the business was performing at the present time, answering questions like "How are we doing right now?" Today the focus of BI users are looking into the future. "Given what I did before and how I am currently doing this quarter, how will I do next quarter?" Furthermore, fuelled by the demands of Big Data, BI systems are going through a time of incredible change. Predictive analytics, high volume data, unstructured data, social data, mobile, consumable analytics, and data visualization are all examples of demands and capabilities that have become critical within just the past few years, and are growing at an unprecedented pace. This book introduces research problems and solutions on various aspects central to next-generation BI systems. It begins with a chapter on an industry perspective on how BI has evolved, and discusses how game-changing trends have drastically reshaped the landscape of BI. One of the game changers is the shift toward the consumerization of BI tools. As a result, for BI tools to be successfully used by business users (rather than IT departments), the tools need a business model, rather than a data model. One chapter of the book surveys four different types of business modeling. However, even with the existence of a business model for users to express queries, the data that can meet the needs are still captured within a data model. The next chapter on vivification addresses the problem of closing the gap, which is often significant, between the business and the data models. Moreover, Big Data forces BI systems to integrate and consolidate multiple, and often wildly different, data sources. One chapter gives an overview of several integration architectures for dealing with the challenges that need to be overcome. While the book so far focuses on the usual structured relational data, the remaining chapters turn to unstructured data, an ever-increasing and important component of Big Data. One chapter on information extraction describes methods for dealing with the extraction of relations from free text and the web. Finally, BI users need tools to visualize and interpret new and complex types of information in a way that is compelling, intuitive, but accurate. The last chapter gives an overview of information visualization for decision support and text.
Spatial Data Management
Spatial database management deals with the storage, indexing, and querying of data with spatial features, such as location and geometric extent. Many applications require the efficient management of spatial data, including Geographic Information Systems, Computer Aided Design, and Location Based Services. The goal of this book is to provide the reader with an overview of spatial data management technology, with an emphasis on indexing and search techniques. It first introduces spatial data models and queries and discusses the main issues of extending a database system to support spatial data. It presents indexing approaches for spatial data, with a focus on the R-tree. Query evaluation and optimization techniques for the most popular spatial query types (selections, nearest neighbor search, and spatial joins) are portrayed for data in Euclidean spaces and spatial networks. The book concludes by demonstrating the ample application of spatial data management technology on a wide range ofrelated application domains: management of spatio-temporal data and high-dimensional feature vectors, multi-criteria ranking, data mining and OLAP, privacy-preserving data publishing, and spatial keyword search. Table of Contents: Introduction / Spatial Data / Indexing / Spatial Query Evaluation / Spatial Networks / Applications of Spatial Data Management Technology
Query Answer Authentication
In data publishing, the owner delegates the role of satisfying user queries to a third-party publisher. As the servers of the publisher may be untrusted or susceptible to attacks, we cannot assume that they would always process queries correctly, hence there is a need for users to authenticate their query answers. This book introduces various notions that the research community has studied for defining the correctness of a query answer. In particular, it is important to guarantee the completeness, authenticity and minimality of the answer, as well as its freshness. We present authentication mechanisms for a wide variety of queries in the context of relational and spatial databases, text retrieval, and data streams. We also explain the cryptographic protocols from which the authentication mechanisms derive their security properties. Table of Contents: Introduction / Cryptography Foundation / Relational Queries / Spatial Queries / Text Search Queries / Data Streams / Conclusion
Deep Web Query Interface Understanding and Integration
There are millions of searchable data sources on the Web and to a large extent their contents can only be reached through their own query interfaces. There is an enormous interest in making the data in these sources easily accessible. There are primarily two general approaches to achieve this objective. The first is to surface the contents of these sources from the deep Web and add the contents to the index of regular search engines. The second is to integrate the searching capabilities of these sources and support integrated access to them. In this book, we introduce the state-of-the-art techniques for extracting, understanding, and integrating the query interfaces of deep Web data sources. These techniques are critical for producing an integrated query interface for each domain. The interface serves as the mediator for searching all data sources in the concerned domain. While query interface integration is only relevant for the deep Web integration approach, the extraction and understanding of query interfaces are critical for both deep Web exploration approaches. This book aims to provide in-depth and comprehensive coverage of the key technologies needed to create high quality integrated query interfaces automatically. The following technical issues are discussed in detail in this book: query interface modeling, query interface extraction, query interface clustering, query interface matching, query interface attribute integration, and query interface integration. Table of Contents: Introduction / Query Interface Representation and Extraction / Query Interface Clustering and Categorization / Query Interface Matching / Query Interface Attribute Integration / Query Interface Integration / Summary and Future Research
Foundations of Data Quality Management
Data quality is one of the most important problems in data management. A database system typically aims to support the creation, maintenance, and use of large amount of data, focusing on the quantity of data. However, real-life data are often dirty: inconsistent, duplicated, inaccurate, incomplete, or stale. Dirty data in a database routinely generate misleading or biased analytical results and decisions, and lead to loss of revenues, credibility and customers. With this comes the need for data quality management. In contrast to traditional data management tasks, data quality management enables the detection and correction of errors in the data, syntactic or semantic, in order to improve the quality of the data and hence, add value to business processes. While data quality has been a longstanding problem for decades, the prevalent use of the Web has increased the risks, on an unprecedented scale, of creating and propagating dirty data. This monograph gives an overview of fundamental issues underlying central aspects of data quality, namely, data consistency, data deduplication, data accuracy, data currency, and information completeness. We promote a uniform logical framework for dealing with these issues, based on data quality rules. The text is organized into seven chapters, focusing on relational data. Chapter One introduces data quality issues. A conditional dependency theory is developed in Chapter Two, for capturing data inconsistencies. It is followed by practical techniques in Chapter 2b for discovering conditional dependencies, and for detecting inconsistencies and repairing data based on conditional dependencies. Matching dependencies are introduced in Chapter Three, as matching rules for data deduplication. A theory of relative information completeness is studied in Chapter Four, revising the classical Closed World Assumption and the Open World Assumption, to characterize incomplete information in the real world. A data currency model is presented in Chapter Five, to identify the current values of entities in a database and to answer queries with the current values, in the absence of reliable timestamps. Finally, interactions between these data quality issues are explored in Chapter Six. Important theoretical results and practical algorithms are covered, but formal proofs are omitted. The bibliographical notes contain pointers to papers in which the results were presented and proven, as well as references to materials for further reading. This text is intended for a seminar course at the graduate level. It is also to serve as a useful resource for researchers and practitioners who are interested in the study of data quality. The fundamental research on data quality draws on several areas, including mathematical logic, computational complexity and database theory. It has raised as many questions as it has answered, and is a rich source of questions and vitality. Table of Contents: Data Quality: An Overview / Conditional Dependencies / Cleaning Data with Conditional Dependencies / Data Deduplication / Information Completeness / Data Currency / Interactions between Data Quality Issues
Outlier Detection for Temporal Data
Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data.In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neuralnetworks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies
Probabilistic Databases
Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques
P2P Techniques for Decentralized Applications
As an alternative to traditional client-server systems, Peer-to-Peer (P2P) systems provide major advantages in terms of scalability, autonomy and dynamic behavior of peers, and decentralization of control. Thus, they are well suited for large-scale data sharing in distributed environments. Most of the existing P2P approaches for data sharing rely on either structured networks (e.g., DHTs) for efficient indexing, or unstructured networks for ease of deployment, or some combination. However, these approaches have some limitations, such as lack of freedom for data placement in DHTs, and high latency and high network traffic in unstructured networks. To address these limitations, gossip protocols which are easy to deploy and scale well, can be exploited. In this book, we will give an overview of these different P2P techniques and architectures, discuss their trade-offs, and illustrate their use for decentralizing several large-scale data sharing applications. Table of Contents: P2P Overlays, Query Routing, and Gossiping / Content Distribution in P2P Systems / Recommendation Systems / Top-k Query Processing in P2P Systems
Similarity Joins in Relational Database Systems
State-of-the-art database systems manage and process a variety of complex objects, including strings and trees. For such objects equality comparisons are often not meaningful and must be replaced by similarity comparisons. This book describes the concepts and techniques to incorporate similarity into database systems. We start out by discussing the properties of strings and trees, and identify the edit distance as the de facto standard for comparing complex objects. Since the edit distance is computationally expensive, token-based distances have been introduced to speed up edit distance computations. The basic idea is to decompose complex objects into sets of tokens that can be compared efficiently. Token-based distances are used to compute an approximation of the edit distance and prune expensive edit distance calculations. A key observation when computing similarity joins is that many of the object pairs, for which the similarity is computed, are very different from each other. Filters exploit this property to improve the performance of similarity joins. A filter preprocesses the input data sets and produces a set of candidate pairs. The distance function is evaluated on the candidate pairs only. We describe the essential query processing techniques for filters based on lower and upper bounds. For token equality joins we describe prefix, size, positional and partitioning filters, which can be used to avoid the computation of small intersections that are not needed since the similarity would be too low.
Incomplete Data and Data Dependencies in Relational Databases
The chase has long been used as a central tool to analyze dependencies and their effect on queries. It has been applied to different relevant problems in database theory such as query optimization, query containment and equivalence, dependency implication, and database schema design. Recent years have seen a renewed interest in the chase as an important tool in several database applications, such as data exchange and integration, query answering in incomplete data, and many others. It is well known that the chase algorithm might be non-terminating and thus, in order for it to find practical applicability, it is crucial to identify cases where its termination is guaranteed. Another important aspect to consider when dealing with the chase is that it can introduce null values into the database, thereby leading to incomplete data. Thus, in several scenarios where the chase is used the problem of dealing with data dependencies and incomplete data arises. This book discusses fundamental issues concerning data dependencies and incomplete data with a particular focus on the chase and its applications in different database areas. We report recent results about the crucial issue of identifying conditions that guarantee the chase termination. Different database applications where the chase is a central tool are discussed with particular attention devoted to query answering in the presence of data dependencies and database schema design. Table of Contents: Introduction / Relational Databases / Incomplete Databases / The Chase Algorithm / Chase Termination / Data Dependencies and Normal Forms / Universal Repairs / Chase and Database Applications
Enterprise, Business-Process and Information Systems Modeling
This book contains the refereed proceedings of two long-running events held along with the CAiSE conference relating to the areas of enterprise, business-process and information systems modeling: * the 23rd International Conference on Business Process Modeling, Development and Support, BPMDS 2022, and * the 27th International Conference on Exploring Modeling Methods for Systems Analysis and Development, EMMSAD 2022. The conferences were taking place in Leuven, Belgium during June 6-7, 2022. For BPMDS 7 full papers and 2 short papers were carefully reviewed and selected for publication from a total of 18 submissions; for EMMSAD 11 full papers and 3 short papers were accepted from 30 submissions after thorough reviews. The papers were organized in topical sections as follows: BPMDS: Actual and perceived challenges; business process modeling; understanding collaboration: one issue, many perspectives; and event logs - why it derivates; EMMSAD: Foundations of modeling and method engineering; enterprise, business process, and capability modeling; information systems and requirements modeling; domain-specific and knowledge modeling; and evaluation of modeling approaches.
Group Decision and Negotiation: Methodological and Practical Issues
Advanced Information Systems Engineering Workshops
This book constitutes the thoroughly refereed proceedings of the international workshops associated with the 34th International Conference on Advanced Information Systems Engineering, CAiSE 2022, which was held in Leuven, Belgium, during June 6-10, 2022.The workshops included in this volume are: - BC4IS: Second International Workshop on Blockchain for Information Systems- ISESL: Second International Workshop on Information Systems Engineering for Smarter Life- KET4DF: 4th International Workshop on Key Enabling Technology for Digital Factories They reflect a broad range of topics and trends ranging from blockchain technologies via digital factories, ethics, and ontologies, to the agile methods for business and information systems. The 11 full papers and 1 short paper presented in this book were carefully reviewed and selected from 23 submissions.
Hands-On Financial Modeling with Excel for Microsoft 365 - Second Edition
Explore a variety of Excel features, functions, and productivity tips for various aspects of financial modelingKey FeaturesExplore Excel's financial functions and pivot tables with this updated second editionBuild an integrated financial model with Excel for Microsoft 365 from scratchPerform financial analysis with the help of real-world use casesBook DescriptionFinancial modeling is a core skill required by anyone who wants to build a career in finance. Hands-On Financial Modeling with Excel for Microsoft 365 explores financial modeling terminologies with the help of Excel.Starting with the key concepts of Excel, such as formulas and functions, this updated second edition will help you to learn all about referencing frameworks and other advanced components for building financial models. As you proceed, you'll explore the advantages of Power Query, learn how to prepare a 3-statement model, inspect your financial projects, build assumptions, and analyze historical data to develop data-driven models and functional growth drivers. Next, you'll learn how to deal with iterations and provide graphical representations of ratios, before covering best practices for effective model testing. Later, you'll discover how to build a model to extract a statement of comprehensive income and financial position, and understand capital budgeting with the help of end-to-end case studies.By the end of this financial modeling Excel book, you'll have examined data from various use cases and have developed the skills you need to build financial models to extract the information required to make informed business decisions.What you will learnIdentify the growth drivers derived from processing historical data in ExcelUse discounted cash flow (DCF) for efficient investment analysisPrepare detailed asset and debt schedule models in ExcelCalculate profitability ratios using various profit parametersObtain and transform data using Power QueryDive into capital budgeting techniquesApply a Monte Carlo simulation to derive key assumptions for your financial modelBuild a financial model by projecting balance sheets and profit and lossWho this book is forThis book is for data professionals, analysts, traders, business owners, and students who want to develop and implement in-demand financial modeling skills in their finance, analysis, trading, and valuation work. Even if you don't have any experience in data and statistics, this book will help you get started with building financial models. Working knowledge of Excel is a prerequisite.Table of ContentsAn Introduction to Financial Modeling and ExcelSteps for Building a Financial ModelFormulas and Functions - Completing Modeling Tasks with a Single FormulaThe Referencing Framework in ExcelAn Introduction to Power QueryUnderstanding Project and Building AssumptionsAsset and Debt SchedulesPreparing a Cash Flow StatementRatio AnalysisValuationModel Testing for Reasonableness and AccuracyCase Study 1 - Building a Model to Extract a Balance Sheet and Profit and Loss from a Trial BalanceCase Study 2 - Creating a Model for Capital Budgeting
The Mathematics of Finite Networks
Since the early eighteenth century, the theory of networks and graphs has matured into an indispensable tool for describing countless real-world phenomena. However, the study of large-scale features of a network often requires unrealistic limits, such as taking the network size to infinity or assuming a continuum. These asymptotic and analytic approaches can significantly diverge from real or simulated networks when applied at the finite scales of real-world applications. This book offers an approach to overcoming these limitations by introducing operator graph theory, an exact, non-asymptotic set of tools combining graph theory with operator calculus. The book is intended for mathematicians, physicists, and other scientists interested in discrete finite systems and their graph-theoretical description, and in delineating the abstract algebraic structures that characterise such systems. All the necessary background on graph theory and operator calculus is included for readers to understand the potential applications of operator graph theory.
SAP S/4hana Central Finance and Group Reporting
Put together a centralised repository for all financial information and alter it as you see fitKey FeaturesExpert-led approach to implementing S/4 Central Finance in a wide range of companies.Solution-focused responses on Central Finance, Group reporting, BPC, and Fiori.Preparation guide for the certification exam and SAP S/4HANA Interview. DescriptionYour SAP S/4HANA journey has just begun. This book details several processes, methods, and expert strategies for integrating central finance into your IT systems, streamlining finance operations, data reporting, and master data preparation.With the help of this book, you'll learn all you need to know to get the most out of SAP S/4HANA Central Finance, SAP Group Reporting, BPC, Fiori, and other cutting-edge technologies. This book includes numerous examples to demonstrate the essentials of SAP S/4HANA Central Finance, SAP Group reporting, BPC, and Fiori. It offers extensive hands-on practice utilizing SAP S/4HANA standards to demonstrate Fiori, BPC, SAP S/4HANA Central Finance, and Group reporting. The book contains many applications and projects from throughout the industry spectrum. Interviewing for a job and passing the SAP Certification exam can be made easier with the help of this book!After reading this book you will be able to perform SAP S/4HANA Central Finance and SAP Group reporting operations. You can also define complex activities in SAP S/4HANA.What you will learnConduct the implementation of Central Finance in your IT environment.Consolidate your SAP S/4HANA system's finances.Carry out currency conversion, intercompany elimination, financial closure, and reporting.Perform a BPC Evaluation.Carry out SAP S/4HANA Central Finance and Group reporting functions.Utilize Fiori applications to perform SAP S/4HANA operations.Who this book is forThis book is a must for SAP consultants, architects, and project managers who wish to become proficient in the SAP S/4HANA project life cycle phases.Table of Contents1. Key success factors for adopting S/4 Central Finance in any organization2. Pragmatic approach - BPC, Fiori, S/4 Central Finance and SAP Group reporting3. Interview questions and answers on BPC, Central Finance and Group reportingRead more
Smart Technologies for Precision Assembly
This open access book constitutes the refereed post-conference proceedings of the 9th IFIP WG 5.5 International Precision Assembly Seminar, IPAS 2020, held virtually in December 2020.The 16 revised full papers and 10 revised short papers presented together with 1 keynote paper were carefully reviewed and selected from numerous submissions. The papers address topics such as assembly design and planning; assembly operations; assembly cells and systems; human centred assembly; and assistance methods in assembly.
Building Data Science Solutions with Anaconda
The missing manual to becoming a successful data scientist-develop the skills to use key tools and the knowledge to thrive in the AI/ML landscapeKey Features: Learn from an AI patent-holding engineering manager with deep experience in Anaconda tools and OSSGet to grips with critical aspects of data science such as bias in datasets and interpretability of modelsGain a deeper understanding of the AI/ML landscape through real-world examples and practical analogiesBook Description: You might already know that there's a wealth of data science and machine learning resources available on the market, but what you might not know is how much is left out by most of these AI resources. This book not only covers everything you need to know about algorithm families but also ensures that you become an expert in everything, from the critical aspects of avoiding bias in data to model interpretability, which have now become must-have skills.In this book, you'll learn how using Anaconda as the easy button, can give you a complete view of the capabilities of tools such as conda, which includes how to specify new channels to pull in any package you want as well as discovering new open source tools at your disposal. You'll also get a clear picture of how to evaluate which model to train and identify when they have become unusable due to drift. Finally, you'll learn about the powerful yet simple techniques that you can use to explain how your model works.By the end of this book, you'll feel confident using conda and Anaconda Navigator to manage dependencies and gain a thorough understanding of the end-to-end data science workflow.What You Will Learn: Install packages and create virtual environments using condaUnderstand the landscape of open source software and assess new toolsUse scikit-learn to train and evaluate model approachesDetect bias types in your data and what you can do to prevent itGrow your skillset with tools such as NumPy, pandas, and Jupyter NotebooksSolve common dataset issues, such as imbalanced and missing dataUse LIME and SHAP to interpret and explain black-box modelsWho this book is for: If you're a data analyst or data science professional looking to make the most of Anaconda's capabilities and deepen your understanding of data science workflows, then this book is for you. You don't need any prior experience with Anaconda, but a working knowledge of Python and data science basics is a must.
Build Talking Apps for Alexa
Voice recognition is here at last. Alexa and other voice assistants have now become widespread and mainstream. Is your app ready for voice interaction? Learn how to develop your own voice applications for Amazon Alexa. Start with techniques for building conversational user interfaces and dialog management. Integrate with existing applications and visual interfaces to complement voice-first applications. The future of human-computer interaction is voice, and we'll help you get ready for it. For decades, voice-enabled computers have only existed in the realm of science fiction. But now the Alexa Skills Kit (ASK) lets you develop your own voice-first applications. Leverage ASK to create engaging and natural user interfaces for your applications, enabling them to listen to users and talk back. You'll see how to use voice and sound as first-class components of user-interface design. We'll start with the essentials of building Alexa voice applications, called skills, including useful tools for creating, testing, and deploying your skills. From there, you can define parameters and dialogs that will prompt users for input in a natural, conversational style. Integrate your Alexa skills with Amazon services and other backend services to create a custom user experience. Discover how to tailor Alexa's voice and language to create more engaging responses and speak in the user's own language. Complement the voice-first experience with visual interfaces for users on screen-based devices. Add options for users to buy upgrades or other products from your application. Once all the pieces are in place, learn how to publish your Alexa skill for everyone to use. Create the future of user interfaces using the Alexa Skills Kit today. What You Need: You will need a computer capable of running the latest version of Node.js, a Git client, and internet access.
