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Scalable Statistical Modeling And Query Processing Over Large Scale Uncertain Databases
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Book Synopsis Scalable Statistical Modeling and Query Processing Over Large Scale Uncertain Databases by : Bhargav Kanagal Shamanna
Download or read book Scalable Statistical Modeling and Query Processing Over Large Scale Uncertain Databases written by Bhargav Kanagal Shamanna and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Query Processing over Uncertain Databases by : Lei Chen
Download or read book Query Processing over Uncertain Databases written by Lei Chen and published by Morgan & Claypool Publishers. This book was released on 2012-12-01 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to measurement errors, transmission lost, or injected noise for privacy protection, uncertainty exists in the data of many real applications. However, query processing techniques for deterministic data cannot be directly applied to uncertain data because they do not have mechanisms to handle data uncertainty. Therefore, efficient and effective manipulation of uncertain data is a practical yet challenging research topic. In this book, we start from the data models for imprecise and uncertain data, move on to defining different semantics for queries on uncertain data, and finally discuss the advanced query processing techniques for various probabilistic queries in uncertain databases. The book serves as a comprehensive guideline for query processing over uncertain databases. Table of Contents: Introduction / Uncertain Data Models / Spatial Query Semantics over Uncertain Data Models / Spatial Query Processing over Uncertain Databases / Conclusion
Book Synopsis Query Processing on Probabilistic Data by : Guy van den Broeck
Download or read book Query Processing on Probabilistic Data written by Guy van den Broeck and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Probabilistic Databases by : Dan Suciu
Download or read book Probabilistic Databases written by Dan Suciu and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book MR_QP written by Harshit Ashokkumar Modi and published by . This book was released on 2020 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: The utility and widespread use of Relational Database Management Systems(RDBMSs) comes not only from its simple, easy-to-understand data model (a rela-tion or a set) but mainly from the ability to write non-procedural queries and their optimization by the system. Queries produce exact answers that match the contents of the database. Query processing of RDBMSs has been researched for more than 4 decades and includes extensions to more complex analysis on data warehouses. In contrast, search has not been addressed by RDBMSs. As the use of other other data types (key-value store, column-store, and graphs to name a few) are becoming popular for modeling to match the data set characteristics, query processing and optimization are becoming important again. The approaches used in RDBMSs, such as cost-based, I/O focused may not be applicable in the same way to new models and queries. Hence, new approaches need to be developed that are suited for the data model used and the expressiveness of the queries to be supported. This thesis addresses query processing of large graphs (or forest) and develops algorithms for query processing as well as develops heuristics for improving the response time using graph characteristics. Although search (unlike RDBMS) has received a lot of attention for graphs, query processing, in contrast, has received very little attention. With the advent of large social networks and other large graphs(e.g., freebase, knowledge and entity graphs), querying to understand the data set and retrieve relevant/exact information becoming critical. This thesis builds on the previous work at the Information Technology laboratory at UTA (IT Lab) to scale query processing to arbitrary-size graphs (or forests)and to exploit parallelism as much as possible. Partitioning (a form of divide and conquer) and Map/Reduce (for parallel processing) are used as basic ingredients for scalability. Partitioning a graph for query processing and computing all answers poses a number of challenges: i) partitioning schemes, ii) scheduling or choosing which partition or partitions to schedule for processing, iii) developing heuristics for reducing the total response time exploiting query and graph characteristics, and iv) importantly,correctness of results. This thesis address all of the above challenges using the map/reduce framework.The choice of map/reduce framework allows us to make partitions based on available resources and optimize parallelism based on the number of partitions to schedule at a time. We use a partitioning strategy that has been shown to be good for substructure discovery. We develop a number of heuristics that are based on query and graph characteristics. The query itself is expressed as a graph without having to cast in some other language. Relational comparison operators, Boolean operators, wild cards, and union queries are supported. There is no restriction on node and edge labels, and uniquely labeled multiple edges are supported. Extensive experimental analysis of the approach (partitioning sizes, algorithm, and heuristics) using large data sets (real-world and synthetic) are shown for speedup, scalability, and ecacy of the heuristicsproposed.
Book Synopsis Frontiers in Massive Data Analysis by : National Research Council
Download or read book Frontiers in Massive Data Analysis written by National Research Council and published by National Academies Press. This book was released on 2013-09-03 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
Book Synopsis Ranking Queries on Uncertain Data by : Ming Hua
Download or read book Ranking Queries on Uncertain Data written by Ming Hua and published by . This book was released on 2009 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertain data is inherent in many important applications, such as environmental surveillance, market analysis, and quantitative economics research. Due to the importance of those applications and rapidly increasing amounts of uncertain data collected and accumulated, analyzing large collections of uncertain data has become an important task. Ranking queries (also known as top-K queries) are often natural and useful in analyzing uncertain data. In this thesis, we study the problem of ranking queries on uncertain data. Specifically, we extend the basic uncertain data model in three directions, including uncertain data streams, probabilistic linkages, and probabilistic graphs, to meet various application needs. Moreover, we develop a series of novel ranking queries on uncertain data at different granularity levels, including selecting the most typical instances within an uncertain object, ranking instances and objects among a set of uncertain objects, and ranking the aggregate sets of uncertain objects. To tackle the challenges on efficiency and scalability, we develop efficient and scalable query evaluation algorithms for the proposed ranking queries. First, we integrate statistical principles and scalable computational techniques to compute exact query results. Second, we develop efficient randomized algorithms to approximate the answers to ranking queries. Third, we propose efficient approximation methods based on the distribution characteristics of query results. A comprehensive empirical study using real and synthetic data sets verifies the effectiveness of the proposed ranking queries and the efficiency of our query evaluation methods.
Book Synopsis Scalable Uncertainty Management by : Salem Benferhat
Download or read book Scalable Uncertainty Management written by Salem Benferhat and published by Springer. This book was released on 2011-10-07 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Conference on Scalable Uncertainty Management, SUM 2011, held in Dayton, OH, USA, in October 2011. The 32 revised full papers and 3 revised short papers presented together with the abstracts of 2 invited talks and 6 “discussant” contributions were carefully reviewed and selected from 58 submissions. The papers are organized in topical sections on argumentation systems, probabilistic inference, dynamic of beliefs, information retrieval and databases, ontologies, possibility theory and classification, logic programming, and applications.
Book Synopsis Databases and Information Systems VIII by : A. Kalja
Download or read book Databases and Information Systems VIII written by A. Kalja and published by IOS Press. This book was released on 2014-12 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Databases and information systems are the backbone of modern information technology and are crucial to the IT systems which support all aspects of our everyday life; from government, education and healthcare, to business processes and the storage of our personal photos and archives. This book presents 22 of the best revised papers accepted following stringent peer review for the 11th International Baltic Conference on Databases and Information Systems (Baltic DB&IS 2014), held in Tallinn, Estonia, in June 2014. The conference provided a forum for the exchange of scientific achievements between the research communities of the Baltic countries and the rest of the world in the area of databases and information systems, bringing together researchers, practitioners and Ph.D. students from many countries. The subject areas covered at the conference focused on big data processing, data warehouses, data integration and services, data and knowledge management, e-government, as well as e-services and e-learning.
Book Synopsis Knowledge Graphs and Big Data Processing by : Valentina Janev
Download or read book Knowledge Graphs and Big Data Processing written by Valentina Janev and published by Springer Nature. This book was released on 2020-07-15 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.
Book Synopsis Handling Resource Constraints and Scalability in Continuous Query Processing by : Junyi Xie
Download or read book Handling Resource Constraints and Scalability in Continuous Query Processing written by Junyi Xie and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent years have witnessed a rapid rise of a new class of data-intensive applications in which data arrive as transient, high-volume streams. Financial data processing, network monitoring, and sensor networks are all examples of such applications. Traditional relational database systems model data as persistent relations, but for this new class of applications, it is more appropriate to model data as unbounded streams with continuously arriving tuples. The stream data model necessitates a new style of queries called continuous queries. Unlike a one-time query executed over a single finite and static database state, a continuous query continuously generates new result tuples as new stream tuples arrive. This dissertation tackles a range of challenges that arise in processing continuous queries. Specifically, for resource-constrained settings, this dissertation proposes techniques for coping with response-time and memory constraints. To scale to a large number of continuous queries running concurrently, this dissertation proposes techniques for indexing continuous queries as data, and processing and optimizing incoming stream tuples as queries over such data. A common theme underlying most of these techniques is exploiting the characteristics of the data and the continuous queries, e.g., asymmetry in the costs of processing different streams, temporal trends in the values of stream attributes, and clusteredness that arises in a large number of continuous queries.
Book Synopsis Scalable Query Evaluation Over Complex Probabilistic Databases by : Abhay Jha
Download or read book Scalable Query Evaluation Over Complex Probabilistic Databases written by Abhay Jha and published by . This book was released on 2012 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: The age of Big Data has brought with itself datasets which are not just big, but also much more complicated. These datasets are constructed from disparate, unreliable and noisy sources, many times in an ad-hoc way because careful data cleaning and integration is too time consuming and not always necessary anymore. Representing the uncertainty hidden in these datasets is necessary to get meaningful query answers and Probabilistic Databases have come up as arguably the most popular solution to this problem. Their application to practical problems though has been held back because (i) the common models they use are not rich enough to capture the dependencies in these problems, and (ii) unlike traditional databases, query evaluation for probabilistic databases can be very expensive and unpredictable. This dissertation addresses these challenges by first proposing a new model for probabilistic databases that is rich enough to capture the dependencies found in most practical applications, while still allowing for a translation to considerably simpler and well-studied models. Our model leverages existing models from AI literature that combine probability theory with logic. The main challenge of query evaluation over probabilistic databases is that it requires solving probabilistic inference which is a notoriously hard problem. This dissertation studies this problem via both (i) foundational results that give new theoretical insights about existing probabilistic inference algorithms, like Read-Once Formulas, Tree-Decompositions, Binary Decision Diagrams, Negation Normal Forms, when applied to the setting of probabilistic databases, which as we will see have their own distinct challenges and expectations, and (ii) building a robust system where the above ideas are leveraged for efficient and reliable query evaluation.
Book Synopsis Global Trends in Computing and Communication Systems by : P. Venkata Krishna
Download or read book Global Trends in Computing and Communication Systems written by P. Venkata Krishna and published by Springer. This book was released on 2012-08-08 with total page 832 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set, CCIS 0269-CCIS 0270, constitutes the refereed post-conference proceedings of the International Conference on Global Trends in Computing and Communication, ObCom 2011, held in Vellore, India, in December 2011. The 173 full papers presented together with a keynote paper and invited papers were carefully reviewed and selected from 842 submissions. The conference addresses all current issues associated with computing, communication and information. The proceedings consists of invited papers dealing with the review of performance models of computer and communication systems and contributed papers that feature topics such as networking, cloud computing, fuzzy logic, mobile communication, image processing, navigation systems, biometrics and Web services covering literally all the vital areas of the computing domains.
Book Synopsis Scientific and Statistical Database Management by : Judith Bayard Cushing
Download or read book Scientific and Statistical Database Management written by Judith Bayard Cushing and published by Springer. This book was released on 2011-07-01 with total page 618 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 23rd International Conference on Scientific and Statistical Database Management, SSDBM 2011, held in Portland, OR, USA, in July 2011. The 26 long and 12 short papers presented together with 15 posters were carefully reviewed and selected from 80 submissions. The topics covered are ranked search; temporal data and queries; workflow and provenance; querying graphs; clustering and data mining; architectures and privacy; and applications and models.
Book Synopsis Scalable Distributed Query Processing in Parallel Main-memory Database Systems by : Wolf-Steffen Rödiger
Download or read book Scalable Distributed Query Processing in Parallel Main-memory Database Systems written by Wolf-Steffen Rödiger and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Readings in Database Systems by : Joseph M. Hellerstein
Download or read book Readings in Database Systems written by Joseph M. Hellerstein and published by MIT Press. This book was released on 2005 with total page 884 pages. Available in PDF, EPUB and Kindle. Book excerpt: The latest edition of a popular text and reference on database research, with substantial new material and revision; covers classical literature and recent hot topics. Lessons from database research have been applied in academic fields ranging from bioinformatics to next-generation Internet architecture and in industrial uses including Web-based e-commerce and search engines. The core ideas in the field have become increasingly influential. This text provides both students and professionals with a grounding in database research and a technical context for understanding recent innovations in the field. The readings included treat the most important issues in the database area--the basic material for any DBMS professional. This fourth edition has been substantially updated and revised, with 21 of the 48 papers new to the edition, four of them published for the first time. Many of the sections have been newly organized, and each section includes a new or substantially revised introduction that discusses the context, motivation, and controversies in a particular area, placing it in the broader perspective of database research. Two introductory articles, never before published, provide an organized, current introduction to basic knowledge of the field; one discusses the history of data models and query languages and the other offers an architectural overview of a database system. The remaining articles range from the classical literature on database research to treatments of current hot topics, including a paper on search engine architecture and a paper on application servers, both written expressly for this edition. The result is a collection of papers that are seminal and also accessible to a reader who has a basic familiarity with database systems.
Book Synopsis Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design by : Laurent, Anne
Download or read book Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design written by Laurent, Anne and published by IGI Global. This book was released on 2009-10-31 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book presents up-to-date techniques for addressing data management problems with logic and memory use"--Provided by publisher.
