Probabilistic Ranking Techniques In Relational Databases

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Probabilistic Ranking Techniques in Relational Databases

Author : Ihab Ilyas
Publisher : Springer Nature
ISBN 13 : 303101846X
Total Pages : 71 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Probabilistic Ranking Techniques in Relational Databases by : Ihab Ilyas

Download or read book Probabilistic Ranking Techniques in Relational Databases written by Ihab Ilyas and published by Springer Nature. This book was released on 2022-05-31 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ranking queries are widely used in data exploration, data analysis and decision making scenarios. While most of the currently proposed ranking techniques focus on deterministic data, several emerging applications involve data that are imprecise or uncertain. Ranking uncertain data raises new challenges in query semantics and processing, making conventional methods inapplicable. Furthermore, the interplay between ranking and uncertainty models introduces new dimensions for ordering query results that do not exist in the traditional settings. This lecture describes new formulations and processing techniques for ranking queries on uncertain data. The formulations are based on marriage of traditional ranking semantics with possible worlds semantics under widely-adopted uncertainty models. In particular, we focus on discussing the impact of tuple-level and attribute-level uncertainty on the semantics and processing techniques of ranking queries. Under the tuple-level uncertainty model, we describe new processing techniques leveraging the capabilities of relational database systems to recognize and handle data uncertainty in score-based ranking. Under the attribute-level uncertainty model, we describe new probabilistic ranking models and a set of query evaluation algorithms, including sampling-based techniques. We also discuss supporting rank join queries on uncertain data, and we show how to extend current rank join methods to handle uncertainty in scoring attributes. Table of Contents: Introduction / Uncertainty Models / Query Semantics / Methodologies / Uncertain Rank Join / Conclusion

Probabilistic Ranking Techniques in Relational Databases

Author : Ihab F. Ilyas
Publisher : Morgan & Claypool Publishers
ISBN 13 : 160845567X
Total Pages : 73 pages
Book Rating : 4.6/5 (84 download)

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Book Synopsis Probabilistic Ranking Techniques in Relational Databases by : Ihab F. Ilyas

Download or read book Probabilistic Ranking Techniques in Relational Databases written by Ihab F. Ilyas and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ranking queries are widely used in data exploration, data analysis and decision making scenarios. While most of the currently proposed ranking techniques focus on deterministic data, several emerging applications involve data that are imprecise or uncertain. Ranking uncertain data raises new challenges in query semantics and processing, making conventional methods inapplicable. Furthermore, the interplay between ranking and uncertainty models introduces new dimensions for ordering query results that do not exist in the traditional settings. This lecture describes new formulations and processing techniques for ranking queries on uncertain data. The formulations are based on marriage of traditional ranking semantics with possible worlds semantics under widely-adopted uncertainty models. In particular, we focus on discussing the impact of tuple-level and attribute-level uncertainty on the semantics and processing techniques of ranking queries. Under the tuple-level uncertainty model, we describe new processing techniques leveraging the capabilities of relational database systems to recognize and handle data uncertainty in score-based ranking. Under the attribute-level uncertainty model, we describe new probabilistic ranking models and a set of query evaluation algorithms, including sampling-based techniques. We also discuss supporting rank join queries on uncertain data, and we show how to extend current rank join methods to handle uncertainty in scoring attributes. Table of Contents: Introduction / Uncertainty Models / Query Semantics / Methodologies / Uncertain Rank Join / Conclusion

Advances on Databases and Information Systems

Author : Tadeusz Morzy
Publisher : Springer
ISBN 13 : 3642330746
Total Pages : 456 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Advances on Databases and Information Systems by : Tadeusz Morzy

Download or read book Advances on Databases and Information Systems written by Tadeusz Morzy and published by Springer. This book was released on 2012-09-13 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed proceedings of the 16th East-European Conference on Advances in Databases and Information Systems (ADBIS 2012), held in Poznan, Poland, in September 2012. The 32 revised full papers presented were carefully selected and reviewed from 122 submissions. The papers cover a wide spectrum of issues concerning the area of database and information systems, including database theory, database architectures, query languages, query processing and optimization, design methods, data integration, view selection, nearest-neighbor searching, analytical query processing, indexing and caching, concurrency control, distributed systems, data mining, data streams, ontology engineering, social networks, multi-agent systems, business process modeling, knowledge management, and application-oriented topics like RFID, XML, and data on the Web.

Probabilistic Databases

Author : Dan Suciu
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1608456803
Total Pages : 183 pages
Book Rating : 4.6/5 (84 download)

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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

Similarity Joins in Relational Database Systems

Author : Nikolaus Augsten
Publisher : Springer Nature
ISBN 13 : 3031018516
Total Pages : 106 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Similarity Joins in Relational Database Systems by : Nikolaus Augsten

Download or read book Similarity Joins in Relational Database Systems written by Nikolaus Augsten and published by Springer Nature. This book was released on 2022-05-31 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : Sergio Greco
Publisher : Springer Nature
ISBN 13 : 3031018931
Total Pages : 111 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Incomplete Data and Data Dependencies in Relational Databases by : Sergio Greco

Download or read book Incomplete Data and Data Dependencies in Relational Databases written by Sergio Greco and published by Springer Nature. This book was released on 2022-06-01 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Probabilistic Databases

Author : Dan Suciu
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1608456811
Total Pages : 182 pages
Book Rating : 4.6/5 (84 download)

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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 2011-07-07 with total page 182 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

Query Processing over Incomplete Databases

Author : Yunjun Gao
Publisher : Springer Nature
ISBN 13 : 303101863X
Total Pages : 106 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Query Processing over Incomplete Databases by : Yunjun Gao

Download or read book Query Processing over Incomplete Databases written by Yunjun Gao and published by Springer Nature. This book was released on 2022-06-01 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incomplete data is part of life and almost all areas of scientific studies. Users tend to skip certain fields when they fill out online forms; participants choose to ignore sensitive questions on surveys; sensors fail, resulting in the loss of certain readings; publicly viewable satellite map services have missing data in many mobile applications; and in privacy-preserving applications, the data is incomplete deliberately in order to preserve the sensitivity of some attribute values. Query processing is a fundamental problem in computer science, and is useful in a variety of applications. In this book, we mostly focus on the query processing over incomplete databases, which involves finding a set of qualified objects from a specified incomplete dataset in order to support a wide spectrum of real-life applications. We first elaborate the three general kinds of methods of handling incomplete data, including (i) discarding the data with missing values, (ii) imputation for the missing values, and (iii) just depending on the observed data values. For the third method type, we introduce the semantics of k-nearest neighbor (kNN) search, skyline query, and top-k dominating query on incomplete data, respectively. In terms of the three representative queries over incomplete data, we investigate some advanced techniques to process incomplete data queries, including indexing, pruning as well as crowdsourcing techniques.

P2P Techniques for Decentralized Applications

Author : Esther Pacitti
Publisher : Springer Nature
ISBN 13 : 3031018885
Total Pages : 90 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis P2P Techniques for Decentralized Applications by : Esther Pacitti

Download or read book P2P Techniques for Decentralized Applications written by Esther Pacitti and published by Springer Nature. This book was released on 2022-06-01 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Data Exploration Using Example-Based Methods

Author : Matteo Lissandrini
Publisher : Springer Nature
ISBN 13 : 3031018664
Total Pages : 146 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Data Exploration Using Example-Based Methods by : Matteo Lissandrini

Download or read book Data Exploration Using Example-Based Methods written by Matteo Lissandrini and published by Springer Nature. This book was released on 2022-06-01 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Methods for Mining and Summarizing Text Conversations

Author : Giuseppe Carenini‌
Publisher : Springer Nature
ISBN 13 : 303101880X
Total Pages : 120 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Methods for Mining and Summarizing Text Conversations by : Giuseppe Carenini‌

Download or read book Methods for Mining and Summarizing Text Conversations written by Giuseppe Carenini‌ and published by Springer Nature. This book was released on 2022-06-01 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the Internet Revolution, human conversational data -- in written forms -- are accumulating at a phenomenal rate. At the same time, improvements in speech technology enable many spoken conversations to be transcribed. Individuals and organizations engage in email exchanges, face-to-face meetings, blogging, texting and other social media activities. The advances in natural language processing provide ample opportunities for these "informal documents" to be analyzed and mined, thus creating numerous new and valuable applications. This book presents a set of computational methods to extract information from conversational data, and to provide natural language summaries of the data. The book begins with an overview of basic concepts, such as the differences between extractive and abstractive summaries, and metrics for evaluating the effectiveness of summarization and various extraction tasks. It also describes some of the benchmark corpora used in the literature. The book introduces extraction and mining methods for performing subjectivity and sentiment detection, topic segmentation and modeling, and the extraction of conversational structure. It also describes frameworks for conducting dialogue act recognition, decision and action item detection, and extraction of thread structure. There is a specific focus on performing all these tasks on conversational data, such as meeting transcripts (which exemplify synchronous conversations) and emails (which exemplify asynchronous conversations). Very recent approaches to deal with blogs, discussion forums and microblogs (e.g., Twitter) are also discussed. The second half of this book focuses on natural language summarization of conversational data. It gives an overview of several extractive and abstractive summarizers developed for emails, meetings, blogs and forums. It also describes attempts for building multi-modal summarizers. Last but not least, the book concludes with thoughts on topics for further development. Table of Contents: Introduction / Background: Corpora and Evaluation Methods / Mining Text Conversations / Summarizing Text Conversations / Conclusions / Final Thoughts

Databases on Modern Hardware

Author : Anastasia Ailamaki
Publisher : Springer Nature
ISBN 13 : 3031018583
Total Pages : 101 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Databases on Modern Hardware by : Anastasia Ailamaki

Download or read book Databases on Modern Hardware written by Anastasia Ailamaki and published by Springer Nature. This book was released on 2022-06-01 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data management systems enable various influential applications from high-performance online services (e.g., social networks like Twitter and Facebook or financial markets) to big data analytics (e.g., scientific exploration, sensor networks, business intelligence). As a result, data management systems have been one of the main drivers for innovations in the database and computer architecture communities for several decades. Recent hardware trends require software to take advantage of the abundant parallelism existing in modern and future hardware. The traditional design of the data management systems, however, faces inherent scalability problems due to its tightly coupled components. In addition, it cannot exploit the full capability of the aggressive micro-architectural features of modern processors. As a result, today's most commonly used server types remain largely underutilized leading to a huge waste of hardware resources and energy. In this book, we shed light on the challenges present while running DBMS on modern multicore hardware. We divide the material into two dimensions of scalability: implicit/vertical and explicit/horizontal. The first part of the book focuses on the vertical dimension: it describes the instruction- and data-level parallelism opportunities in a core coming from the hardware and software side. In addition, it examines the sources of under-utilization in a modern processor and presents insights and hardware/software techniques to better exploit the microarchitectural resources of a processor by improving cache locality at the right level of the memory hierarchy. The second part focuses on the horizontal dimension, i.e., scalability bottlenecks of database applications at the level of multicore and multisocket multicore architectures. It first presents a systematic way of eliminating such bottlenecks in online transaction processing workloads, which is based on minimizing unbounded communication, and shows several techniques that minimize bottlenecks in major components of database management systems. Then, it demonstrates the data and work sharing opportunities for analytical workloads, and reviews advanced scheduling mechanisms that are aware of nonuniform memory accesses and alleviate bandwidth saturation.

Data Cleaning

Author : Venkatesh Ganti
Publisher : Springer Nature
ISBN 13 : 3031018974
Total Pages : 69 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Data Cleaning by : Venkatesh Ganti

Download or read book Data Cleaning written by Venkatesh Ganti and published by Springer Nature. This book was released on 2022-05-31 with total page 69 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data warehouses consolidate various activities of a business and often form the backbone for generating reports that support important business decisions. Errors in data tend to creep in for a variety of reasons. Some of these reasons include errors during input data collection and errors while merging data collected independently across different databases. These errors in data warehouses often result in erroneous upstream reports, and could impact business decisions negatively. Therefore, one of the critical challenges while maintaining large data warehouses is that of ensuring the quality of data in the data warehouse remains high. The process of maintaining high data quality is commonly referred to as data cleaning. In this book, we first discuss the goals of data cleaning. Often, the goals of data cleaning are not well defined and could mean different solutions in different scenarios. Toward clarifying these goals, we abstract out a common set of data cleaning tasks that often need to be addressed. This abstraction allows us to develop solutions for these common data cleaning tasks. We then discuss a few popular approaches for developing such solutions. In particular, we focus on an operator-centric approach for developing a data cleaning platform. The operator-centric approach involves the development of customizable operators that could be used as building blocks for developing common solutions. This is similar to the approach of relational algebra for query processing. The basic set of operators can be put together to build complex queries. Finally, we discuss the development of custom scripts which leverage the basic data cleaning operators along with relational operators to implement effective solutions for data cleaning tasks.

Data-Intensive Workflow Management

Author : Daniel Oliveira
Publisher : Springer Nature
ISBN 13 : 3031018729
Total Pages : 161 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Data-Intensive Workflow Management by : Daniel Oliveira

Download or read book Data-Intensive Workflow Management written by Daniel Oliveira and published by Springer Nature. This book was released on 2022-06-01 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Workflows may be defined as abstractions used to model the coherent flow of activities in the context of an in silico scientific experiment. They are employed in many domains of science such as bioinformatics, astronomy, and engineering. Such workflows usually present a considerable number of activities and activations (i.e., tasks associated with activities) and may need a long time for execution. Due to the continuous need to store and process data efficiently (making them data-intensive workflows), high-performance computing environments allied to parallelization techniques are used to run these workflows. At the beginning of the 2010s, cloud technologies emerged as a promising environment to run scientific workflows. By using clouds, scientists have expanded beyond single parallel computers to hundreds or even thousands of virtual machines. More recently, Data-Intensive Scalable Computing (DISC) frameworks (e.g., Apache Spark and Hadoop) and environments emerged and are being used to execute data-intensive workflows. DISC environments are composed of processors and disks in large-commodity computing clusters connected using high-speed communications switches and networks. The main advantage of DISC frameworks is that they support and grant efficient in-memory data management for large-scale applications, such as data-intensive workflows. However, the execution of workflows in cloud and DISC environments raise many challenges such as scheduling workflow activities and activations, managing produced data, collecting provenance data, etc. Several existing approaches deal with the challenges mentioned earlier. This way, there is a real need for understanding how to manage these workflows and various big data platforms that have been developed and introduced. As such, this book can help researchers understand how linking workflow management with Data-Intensive Scalable Computing can help in understanding and analyzing scientific big data. In this book, we aim to identify and distill the body of work on workflow management in clouds and DISC environments. We start by discussing the basic principles of data-intensive scientific workflows. Next, we present two workflows that are executed in a single site and multi-site clouds taking advantage of provenance. Afterward, we go towards workflow management in DISC environments, and we present, in detail, solutions that enable the optimized execution of the workflow using frameworks such as Apache Spark and its extensions.

Data Processing on FPGAs

Author : Jens Teubner
Publisher : Springer Nature
ISBN 13 : 3031018494
Total Pages : 104 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Data Processing on FPGAs by : Jens Teubner

Download or read book Data Processing on FPGAs written by Jens Teubner and published by Springer Nature. This book was released on 2022-05-31 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Principles of Data Integration

Author : AnHai Doan
Publisher : Elsevier
ISBN 13 : 0123914795
Total Pages : 522 pages
Book Rating : 4.1/5 (239 download)

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Book Synopsis Principles of Data Integration by : AnHai Doan

Download or read book Principles of Data Integration written by AnHai Doan and published by Elsevier. This book was released on 2012-06-25 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles of Data Integration is the first comprehensive textbook of data integration, covering theoretical principles and implementation issues as well as current challenges raised by the semantic web and cloud computing. The book offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand. Readers will also learn how to build their own algorithms and implement their own data integration application. Written by three of the most respected experts in the field, this book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. This text is an ideal resource for database practitioners in industry, including data warehouse engineers, database system designers, data architects/enterprise architects, database researchers, statisticians, and data analysts; students in data analytics and knowledge discovery; and other data professionals working at the R&D and implementation levels. Offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand Enables you to build your own algorithms and implement your own data integration applications

Ranking Queries on Uncertain Data

Author : Ming Hua
Publisher : Springer Science & Business Media
ISBN 13 : 1441993800
Total Pages : 224 pages
Book Rating : 4.4/5 (419 download)

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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 Springer Science & Business Media. This book was released on 2011-03-28 with total page 224 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. Ranking Queries on Uncertain Data discusses the motivations/applications, challenging problems, the fundamental principles, and the evaluation algorithms of ranking queries on uncertain data. Theoretical and algorithmic results of ranking queries on uncertain data are presented in the last section of this book. Ranking Queries on Uncertain Data is the first book to systematically discuss the problem of ranking queries on uncertain data.