Deep Web Query Interface Understanding And Integration

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Deep Web Query Interface Understanding and Integration

Author : Eduard C. Dragut
Publisher : Springer Nature
ISBN 13 : 3031018893
Total Pages : 150 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Deep Web Query Interface Understanding and Integration by : Eduard C. Dragut

Download or read book Deep Web Query Interface Understanding and Integration written by Eduard C. Dragut and published by Springer Nature. This book was released on 2022-05-31 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Deep Web Query Interface Understanding and Integration

Author : Eduard C. Dragut
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1608458954
Total Pages : 170 pages
Book Rating : 4.6/5 (84 download)

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Book Synopsis Deep Web Query Interface Understanding and Integration by : Eduard C. Dragut

Download or read book Deep Web Query Interface Understanding and Integration written by Eduard C. Dragut and published by Morgan & Claypool Publishers. This book was released on 2012-06-01 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

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.

Community Search over Big Graphs

Author : Xin Huang
Publisher : Springer Nature
ISBN 13 : 3031018745
Total Pages : 188 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Community Search over Big Graphs by : Xin Huang

Download or read book Community Search over Big Graphs written by Xin Huang and published by Springer Nature. This book was released on 2022-05-31 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Communities serve as basic structural building blocks for understanding the organization of many real-world networks, including social, biological, collaboration, and communication networks. Recently, community search over graphs has attracted significantly increasing attention, from small, simple, and static graphs to big, evolving, attributed, and location-based graphs. In this book, we first review the basic concepts of networks, communities, and various kinds of dense subgraph models. We then survey the state of the art in community search techniques on various kinds of networks across different application areas. Specifically, we discuss cohesive community search, attributed community search, social circle discovery, and geo-social group search. We highlight the challenges posed by different community search problems. We present their motivations, principles, methodologies, algorithms, and applications, and provide a comprehensive comparison of the existing techniques. This book finally concludes by listing publicly available real-world datasets and useful tools for facilitating further research, and by offering further readings and future directions of research in this important and growing area.

Instant Recovery with Write-Ahead Logging

Author : Goetz Graefe
Publisher : Springer Nature
ISBN 13 : 3031018524
Total Pages : 77 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Instant Recovery with Write-Ahead Logging by : Goetz Graefe

Download or read book Instant Recovery with Write-Ahead Logging written by Goetz Graefe and published by Springer Nature. This book was released on 2014-12-09 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traditional theory and practice of write-ahead logging and of database recovery techniques revolve around three failure classes: transaction failures resolved by rollback; system failures (typically software faults) resolved by restart with log analysis, “redo,” and “undo” phases; and media failures (typically hardware faults) resolved by restore operations that combine multiple types of backups and log replay. The recent addition of single-page failures and single-page recovery has opened new opportunities far beyond its original aim of immediate, lossless repair of single-page wear-out in novel or traditional storage hardware. In the contexts of system and media failures, efficient single-page recovery enables on-demand incremental “redo” and “undo” as part of system restart or media restore operations. This can give the illusion of practically instantaneous restart and restore: instant restart permits processing new queries and updates seconds after system reboot and instant restore permits resuming queries and updates on empty replacement media as if those were already fully recovered. In addition to these instant recovery techniques, the discussion introduces much faster offline restore operations without slowdown in backup operations and with hardly any slowdown in log archiving operations. The new restore techniques also render differential and incremental backups obsolete, complete backup commands on the database server practically instantly, and even permit taking full backups without imposing any load on the database server. Table of Contents: Preface / Acknowledgments / Introduction / Related Prior Work / Single-Page Recovery / Applications of Single-Page Recovery / Instant Restart after a System Failure / Single-Pass Restore / Applications of Single-Pass Restore / Instant Restore after a Media Failure / Multiple Failures / Conclusions / References / Author Biographies

Data Profiling

Author : Ziawasch Abedjan
Publisher : Springer Nature
ISBN 13 : 3031018656
Total Pages : 136 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Data Profiling by : Ziawasch Abedjan

Download or read book Data Profiling written by Ziawasch Abedjan and published by Springer Nature. This book was released on 2022-06-01 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data profiling refers to the activity of collecting data about data, {i.e.}, metadata. Most IT professionals and researchers who work with data have engaged in data profiling, at least informally, to understand and explore an unfamiliar dataset or to determine whether a new dataset is appropriate for a particular task at hand. Data profiling results are also important in a variety of other situations, including query optimization, data integration, and data cleaning. Simple metadata are statistics, such as the number of rows and columns, schema and datatype information, the number of distinct values, statistical value distributions, and the number of null or empty values in each column. More complex types of metadata are statements about multiple columns and their correlation, such as candidate keys, functional dependencies, and other types of dependencies. This book provides a classification of the various types of profilable metadata, discusses popular data profiling tasks, and surveys state-of-the-art profiling algorithms. While most of the book focuses on tasks and algorithms for relational data profiling, we also briefly discuss systems and techniques for profiling non-relational data such as graphs and text. We conclude with a discussion of data profiling challenges and directions for future work in this area.

Datalog and Logic Databases

Author : Sergio Greco
Publisher : Springer Nature
ISBN 13 : 3031018540
Total Pages : 155 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Datalog and Logic Databases by : Sergio Greco

Download or read book Datalog and Logic Databases written by Sergio Greco and published by Springer Nature. This book was released on 2022-05-31 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Cloud-Based RDF Data Management

Author : Zoi Kaoudi
Publisher : Springer Nature
ISBN 13 : 3031018753
Total Pages : 91 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Cloud-Based RDF Data Management by : Zoi Kaoudi

Download or read book Cloud-Based RDF Data Management written by Zoi Kaoudi and published by Springer Nature. This book was released on 2022-05-31 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: Resource Description Framework (or RDF, in short) is set to deliver many of the original semi-structured data promises: flexible structure, optional schema, and rich, flexible Universal Resource Identifiers as a basis for information sharing. Moreover, RDF is uniquely positioned to benefit from the efforts of scientific communities studying databases, knowledge representation, and Web technologies. As a consequence, the RDF data model is used in a variety of applications today for integrating knowledge and information: in open Web or government data via the Linked Open Data initiative, in scientific domains such as bioinformatics, and more recently in search engines and personal assistants of enterprises in the form of knowledge graphs. Managing such large volumes of RDF data is challenging due to the sheer size, heterogeneity, and complexity brought by RDF reasoning. To tackle the size challenge, distributed architectures are required. Cloud computing is an emerging paradigm massively adopted in many applications requiring distributed architectures for the scalability, fault tolerance, and elasticity features it provides. At the same time, interest in massively parallel processing has been renewed by the MapReduce model and many follow-up works, which aim at simplifying the deployment of massively parallel data management tasks in a cloud environment. In this book, we study the state-of-the-art RDF data management in cloud environments and parallel/distributed architectures that were not necessarily intended for the cloud, but can easily be deployed therein. After providing a comprehensive background on RDF and cloud technologies, we explore four aspects that are vital in an RDF data management system: data storage, query processing, query optimization, and reasoning. We conclude the book with a discussion on open problems and future directions.

Human Interaction with Graphs

Author : Sourav S. Bhowmick
Publisher : Springer Nature
ISBN 13 : 3031018613
Total Pages : 186 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Human Interaction with Graphs by : Sourav S. Bhowmick

Download or read book Human Interaction with Graphs written by Sourav S. Bhowmick and published by Springer Nature. This book was released on 2022-06-01 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interacting with graphs using queries has emerged as an important research problem for real-world applications that center on large graph data. Given the syntactic complexity of graph query languages (e.g., SPARQL, Cypher), visual graph query interfaces make it easy for non-programmers to query such graph data repositories. In this book, we present recent developments in the emerging area of visual graph querying paradigm that bridges traditional graph querying with human computer interaction (HCI). Specifically, we focus on techniques that emphasize deep integration between the visual graph query interface and the underlying graph query engine. We discuss various strategies and guidance for constructing graph queries visually, interleaving processing of graph queries and visual actions, visual exploration of graph query results, and automated performance study of visual graph querying frameworks. In addition, this book highlights open problems and new research directions. In summary, in this book, we review and summarize the research thus far into the integration of HCI and graph querying to facilitate user-friendly interaction with graph-structured data, giving researchers a snapshot of the current state of the art in this topic, and future research directions.

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.

Information and Influence Propagation in Social Networks

Author : Wei Chen
Publisher : Springer Nature
ISBN 13 : 3031018508
Total Pages : 161 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Information and Influence Propagation in Social Networks by : Wei Chen

Download or read book Information and Influence Propagation in Social Networks written by Wei Chen and published by Springer Nature. This book was released on 2022-05-31 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research on social networks has exploded over the last decade. To a large extent, this has been fueled by the spectacular growth of social media and online social networking sites, which continue growing at a very fast pace, as well as by the increasing availability of very large social network datasets for purposes of research. A rich body of this research has been devoted to the analysis of the propagation of information, influence, innovations, infections, practices and customs through networks. Can we build models to explain the way these propagations occur? How can we validate our models against any available real datasets consisting of a social network and propagation traces that occurred in the past? These are just some questions studied by researchers in this area. Information propagation models find applications in viral marketing, outbreak detection, finding key blog posts to read in order to catch important stories, finding leaders or trendsetters, information feed ranking, etc. A number of algorithmic problems arising in these applications have been abstracted and studied extensively by researchers under the garb of influence maximization. This book starts with a detailed description of well-established diffusion models, including the independent cascade model and the linear threshold model, that have been successful at explaining propagation phenomena. We describe their properties as well as numerous extensions to them, introducing aspects such as competition, budget, and time-criticality, among many others. We delve deep into the key problem of influence maximization, which selects key individuals to activate in order to influence a large fraction of a network. Influence maximization in classic diffusion models including both the independent cascade and the linear threshold models is computationally intractable, more precisely #P-hard, and we describe several approximation algorithms and scalable heuristics that have been proposed in the literature. Finally, we also deal with key issues that need to be tackled in order to turn this research into practice, such as learning the strength with which individuals in a network influence each other, as well as the practical aspects of this research including the availability of datasets and software tools for facilitating research. We conclude with a discussion of various research problems that remain open, both from a technical perspective and from the viewpoint of transferring the results of research into industry strength applications.

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.

On Transactional Concurrency Control

Author : Goetz Graefe
Publisher : Springer Nature
ISBN 13 : 3031018737
Total Pages : 383 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis On Transactional Concurrency Control by : Goetz Graefe

Download or read book On Transactional Concurrency Control written by Goetz Graefe and published by Springer Nature. This book was released on 2022-05-31 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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.

The Four Generations of Entity Resolution

Author : George Papadakis
Publisher : Springer Nature
ISBN 13 : 3031018788
Total Pages : 152 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis The Four Generations of Entity Resolution by : George Papadakis

Download or read book The Four Generations of Entity Resolution written by George Papadakis and published by Springer Nature. This book was released on 2022-06-01 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Entity Resolution (ER) lies at the core of data integration and cleaning and, thus, a bulk of the research examines ways for improving its effectiveness and time efficiency. The initial ER methods primarily target Veracity in the context of structured (relational) data that are described by a schema of well-known quality and meaning. To achieve high effectiveness, they leverage schema, expert, and/or external knowledge. Part of these methods are extended to address Volume, processing large datasets through multi-core or massive parallelization approaches, such as the MapReduce paradigm. However, these early schema-based approaches are inapplicable to Web Data, which abound in voluminous, noisy, semi-structured, and highly heterogeneous information. To address the additional challenge of Variety, recent works on ER adopt a novel, loosely schema-aware functionality that emphasizes scalability and robustness to noise. Another line of present research focuses on the additional challenge of Velocity, aiming to process data collections of a continuously increasing volume. The latest works, though, take advantage of the significant breakthroughs in Deep Learning and Crowdsourcing, incorporating external knowledge to enhance the existing words to a significant extent. This synthesis lecture organizes ER methods into four generations based on the challenges posed by these four Vs. For each generation, we outline the corresponding ER workflow, discuss the state-of-the-art methods per workflow step, and present current research directions. The discussion of these methods takes into account a historical perspective, explaining the evolution of the methods over time along with their similarities and differences. The lecture also discusses the available ER tools and benchmark datasets that allow expert as well as novice users to make use of the available solutions.

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.

Data Management in Machine Learning Systems

Author : Matthias Boehm
Publisher : Springer Nature
ISBN 13 : 3031018699
Total Pages : 157 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Data Management in Machine Learning Systems by : Matthias Boehm

Download or read book Data Management in Machine Learning Systems written by Matthias Boehm and published by Springer Nature. This book was released on 2022-05-31 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.