Scalability Challenges In Web Search Engines
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Scalability Challenges In Web Search Engines
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Author : B. Barla Cambazoglu
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Scalability Challenges In Web Search Engines written by B. Barla Cambazoglu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
In this book, we aim to provide a fairly comprehensive overview of the scalability and efficiency challenges in large-scale web search engines. More specifically, we cover the issues involved in the design of three separate systems that are commonly available in every web-scale search engine: web crawling, indexing, and query processing systems. We present the performance challenges encountered in these systems and review a wide range of design alternatives employed as solution to these challenges, specifically focusing on algorithmic and architectural optimizations. We discuss the available optimizations at different computational granularities, ranging from a single computer node to a collection of data centers. We provide some hints to both the practitioners and theoreticians involved in the field about the way large-scale web search engines operate and the adopted design choices. Moreover, we survey the efficiency literature, providing pointers to a large number of relatively important research papers. Finally, we discuss some open research problems in the context of search engine efficiency.
Advanced Topics In Information Retrieval
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Author : Massimo Melucci
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-06-10
Advanced Topics In Information Retrieval written by Massimo Melucci and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-10 with Computers categories.
Information retrieval is the science concerned with the effective and efficient retrieval of documents starting from their semantic content. It is employed to fulfill some information need from a large number of digital documents. Given the ever-growing amount of documents available and the heterogeneous data structures used for storage, information retrieval has recently faced and tackled novel applications. In this book, Melucci and Baeza-Yates present a wide-spectrum illustration of recent research results in advanced areas related to information retrieval. Readers will find chapters on e.g. aggregated search, digital advertising, digital libraries, discovery of spam and opinions, information retrieval in context, multimedia resource discovery, quantum mechanics applied to information retrieval, scalability challenges in web search engines, and interactive information retrieval evaluation. All chapters are written by well-known researchers, are completely self-contained and comprehensive, and are complemented by an integrated bibliography and subject index. With this selection, the editors provide the most up-to-date survey of topics usually not addressed in depth in traditional (text)books on information retrieval. The presentation is intended for a wide audience of people interested in information retrieval: undergraduate and graduate students, post-doctoral researchers, lecturers, and industrial researchers.
Scalability Challenges In Web Search Engines
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Author : B. Barla Cambazoglu
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2015-12-01
Scalability Challenges In Web Search Engines written by B. Barla Cambazoglu and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-01 with Computers categories.
In this book, we aim to provide a fairly comprehensive overview of the scalability and efficiency challenges in large-scale web search engines. More specifically, we cover the issues involved in the design of three separate systems that are commonly available in every web-scale search engine: web crawling, indexing, and query processing systems. We present the performance challenges encountered in these systems and review a wide range of design alternatives employed as solution to these challenges, specifically focusing on algorithmic and architectural optimizations. We discuss the available optimizations at different computational granularities, ranging from a single computer node to a collection of data centers. We provide some hints to both the practitioners and theoreticians involved in the field about the way large-scale web search engines operate and the adopted design choices. Moreover, we survey the efficiency literature, providing pointers to a large number of relatively important research papers. Finally, we discuss some open research problems in the context of search engine efficiency.
Social Monitoring For Public Health
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Author : Michael J. Paul
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Social Monitoring For Public Health written by Michael J. Paul and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Public health thrives on high-quality evidence, yet acquiring meaningful data on a population remains a central challenge of public health research and practice. Social monitoring, the analysis of social media and other user-generated web data, has brought advances in the way we leverage population data to understand health. Social media offers advantages over traditional data sources, including real-time data availability, ease of access, and reduced cost. Social media allows us to ask, and answer, questions we never thought possible. This book presents an overview of the progress on uses of social monitoring to study public health over the past decade. We explain available data sources, common methods, and survey research on social monitoring in a wide range of public health areas. Our examples come from topics such as disease surveillance, behavioral medicine, and mental health, among others. We explore the limitations and concerns of these methods. Our survey of this exciting new field of data-driven research lays out future research directions.
The Practice Of Crowdsourcing
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Author : Omar Alonso
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
The Practice Of Crowdsourcing written by Omar Alonso and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Many data-intensive applications that use machine learning or artificial intelligence techniques depend on humans providing the initial dataset, enabling algorithms to process the rest or for other humans to evaluate the performance of such algorithms. Not only can labeled data for training and evaluation be collected faster, cheaper, and easier than ever before, but we now see the emergence of hybrid human-machine software that combines computations performed by humans and machines in conjunction. There are, however, real-world practical issues with the adoption of human computation and crowdsourcing. Building systems and data processing pipelines that require crowd computing remains difficult. In this book, we present practical considerations for designing and implementing tasks that require the use of humans and machines in combination with the goal of producing high-quality labels.
Cognitively Informed Systems Utilizing Practical Approaches To Enrich Information Presentation And Transfer
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Author : Alkhalifa, Eshaa
language : en
Publisher: IGI Global
Release Date : 2006-01-31
Cognitively Informed Systems Utilizing Practical Approaches To Enrich Information Presentation And Transfer written by Alkhalifa, Eshaa and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-01-31 with Technology & Engineering categories.
"This book identifies the main areas of cognitive science and for each area, how different system designs benefit from the findings made in that area"--Provided by publisher.
Dynamic Information Retrieval Modeling
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Author : Grace Hui Yang
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Dynamic Information Retrieval Modeling written by Grace Hui Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Big data and human-computer information retrieval (HCIR) are changing IR. They capture the dynamic changes in the data and dynamic interactions of users with IR systems. A dynamic system is one which changes or adapts over time or a sequence of events. Many modern IR systems and data exhibit these characteristics which are largely ignored by conventional techniques. What is missing is an ability for the model to change over time and be responsive to stimulus. Documents, relevance, users and tasks all exhibit dynamic behavior that is captured in data sets typically collected over long time spans and models need to respond to these changes. Additionally, the size of modern datasets enforces limits on the amount of learning a system can achieve. Further to this, advances in IR interface, personalization and ad display demand models that can react to users in real time and in an intelligent, contextual way. In this book we provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling, the statistical modeling of IR systems that can adapt to change. We define dynamics, what it means within the context of IR and highlight examples of problems where dynamics play an important role. We cover techniques ranging from classic relevance feedback to the latest applications of partially observable Markov decision processes (POMDPs) and a handful of useful algorithms and tools for solving IR problems incorporating dynamics. The theoretical component is based around the Markov Decision Process (MDP), a mathematical framework taken from the field of Artificial Intelligence (AI) that enables us to construct models that change according to sequential inputs. We define the framework and the algorithms commonly used to optimize over it and generalize it to the case where the inputs aren't reliable. We explore the topic of reinforcement learning more broadly and introduce another tool known as a Multi-Armed Bandit which is useful for cases where exploring model parameters is beneficial. Following this we introduce theories and algorithms which can be used to incorporate dynamics into an IR model before presenting an array of state-of-the-art research that already does, such as in the areas of session search and online advertising. Change is at the heart of modern Information Retrieval systems and this book will help equip the reader with the tools and knowledge needed to understand Dynamic Information Retrieval Modeling.
Learning From Multiple Social Networks
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Author : Liqiang Nie
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Learning From Multiple Social Networks written by Liqiang Nie and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
With the proliferation of social network services, more and more social users, such as individuals and organizations, are simultaneously involved in multiple social networks for various purposes. In fact, multiple social networks characterize the same social users from different perspectives, and their contexts are usually consistent or complementary rather than independent. Hence, as compared to using information from a single social network, appropriate aggregation of multiple social networks offers us a better way to comprehensively understand the given social users. Learning across multiple social networks brings opportunities to new services and applications as well as new insights on user online behaviors, yet it raises tough challenges: (1) How can we map different social network accounts to the same social users? (2) How can we complete the item-wise and block-wise missing data? (3) How can we leverage the relatedness among sources to strengthen the learning performance? And (4) How can we jointly model the dual-heterogeneities: multiple tasks exist for the given application and each task has various features from multiple sources? These questions have been largely unexplored to date. We noticed this timely opportunity, and in this book we present some state-of-the-art theories and novel practical applications on aggregation of multiple social networks. In particular, we first introduce multi-source dataset construction. We then introduce how to effectively and efficiently complete the item-wise and block-wise missing data, which are caused by the inactive social users in some social networks. We next detail the proposed multi-source mono-task learning model and its application in volunteerism tendency prediction. As a counterpart, we also present a mono-source multi-task learning model and apply it to user interest inference. We seamlessly unify these models with the so-called multi-source multi-task learning, and demonstrate several application scenarios, such as occupation prediction. Finally, we conclude the book and figure out the future research directions in multiple social network learning, including the privacy issues and source complementarity modeling. This is preliminary research on learning from multiple social networks, and we hope it can inspire more active researchers to work on this exciting area. If we have seen further it is by standing on the shoulders of giants.
The Notion Of Relevance In Information Science
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Author : Tefko Saracevic
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
The Notion Of Relevance In Information Science written by Tefko Saracevic and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Everybody knows what relevance is. It is a "ya'know" notion, concept, idea–no need to explain whatsoever. Searching for relevant information using information technology (IT) became a ubiquitous activity in contemporary information society. Relevant information means information that pertains to the matter or problem at hand—it is directly connected with effective communication. The purpose of this book is to trace the evolution and with it the history of thinking and research on relevance in information science and related fields from the human point of view. The objective is to synthesize what we have learned about relevance in several decades of investigation about the notion in information science. This book deals with how people deal with relevance—it does not cover how systems deal with relevance; it does not deal with algorithms. Spurred by advances in information retrieval (IR) and information systems of various kinds in handling of relevance, a number of basic questions are raised: But what is relevance to start with? What are some of its properties and manifestations? How do people treat relevance? What affects relevance assessments? What are the effects of inconsistent human relevance judgments on tests of relative performance of different IR algorithms or approaches? These general questions are discussed in detail.
Framing Privacy In Digital Collections With Ethical Decision Making
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Author : Virginia Dressler
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Framing Privacy In Digital Collections With Ethical Decision Making written by Virginia Dressler and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
As digital collections continue to grow, the underlying technologies to serve up content also continue to expand and develop. As such, new challenges are presented which continue to test ethical ideologies in everyday environs of the practitioner. There are currently no solid guidelines or overarching codes of ethics to address such issues. The digitization of modern archival collections, in particular, presents interesting conundrums when factors of privacy are weighed and reviewed in both small and mass digitization initiatives. Ethical decision making needs to be present at the onset of project planning in digital projects of all sizes, and we also need to identify the role and responsibility of the practitioner to make more virtuous decisions on behalf of those with no voice or awareness of potential privacy breaches. In this book, notions of what constitutes private information are discussed, as is the potential presence of such information in both analog and digital collections. This book lays groundwork to introduce the topic of privacy within digital collections by providing some examples from documented real-world scenarios and making recommendations for future research. A discussion of the notion privacy as concept will be included, as well as some historical perspective (with perhaps one the most cited work on this topic, for example, Warren and Brandeis' "Right to Privacy," 1890). Concepts from the The Right to Be Forgotten case in 2014 (Google Spain SL, Google Inc. v Agencia Españla de Protección de Datos, Mario Costeja González) are discussed as to how some lessons may be drawn from the response in Europe and also how European data privacy laws have been applied. The European ideologies are contrasted with the Right to Free Speech in the First Amendment in the U.S., highlighting the complexities in setting guidelines and practices revolving around privacy issues when applied to real life scenarios. Two ethical theories are explored: Consequentialism and Deontological. Finally, ethical decision making models will also be applied to our framework of digital collections. Three case studies are presented to illustrate how privacy can be defined within digital collections in some real-world examples.