Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 03. März 2020)
1O'Neil, C.: Angriff der Algorithmen : wie sie Wahlen manipulieren, Berufschancen zerstören und unsere Gesundheit gefährden.Aus dem Englischen von Karsten Petersen.
München : Hanser, 2017. 336 S.
Abstract: Algorithmen nehmen Einfluss auf unser Leben: Von ihnen hängt es ab, ob man etwa einen Kredit für sein Haus erhält und wie viel man für die Krankenversicherung bezahlt. Cathy O'Neil, ehemalige Hedgefonds-Managerin und heute Big-Data-Whistleblowerin, erklärt, wie Algorithmen in der Theorie objektive Entscheidungen ermöglichen, im wirklichen Leben aber mächtigen Interessen folgen. Algorithmen nehmen Einfluss auf die Politik, gefährden freie Wahlen und manipulieren über soziale Netzwerke sogar die Demokratie. Cathy O'Neils dringlicher Appell zeigt, wie sie Diskriminierung und Ungleichheit verstärken und so zu Waffen werden, die das Fundament unserer Gesellschaft erschüttern. ; A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life - and threaten to rip apart our social fabric. We live in the age of the algorithm. Increasingly, the decisions that affect our lives - where we go to school, whether we get a loan, how much we pay for insurance - are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: everyone is judged according to the same rules, and bias is eliminated. And yet, as Cathy O'Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and incontestable, even when they're wrong. Most troubling, they reinforce discrimination. Tracing the arc of a person's life, O'Neil exposes the black box models that shape our future, both as individuals and as a society. These "weapons of math destruction" score teachers and students, sort CVs, grant or deny loans, evaluate workers, target voters, and monitor our health. O'Neil calls on modellers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it's up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.
Inhalt: Kommentare: 'Fascinating and deeply disturbing' - Yuval Noah Harari, Guardian Books of the Year 'A manual for the 21st-century citizen... accessible, refreshingly critical, relevant and urgent' - Federica Cocco, Financial Times
Anmerkung: Originaltitel: Weapons of math destruction:: how Big Data increases inequality and threatens democracy. Vgl. auch den Rezensions-Beitrag: Krüger, J.: Wie der Mensch die Kontrolle über den Algorithmus behalten kann. [19.01.2018]. In: https://netzpolitik.org/2018/algorithmen-regulierung-im-kontext-aktueller-gesetzgebung/.
LCSH: Big data / Social aspects / United States ; Big data / Political aspects / United States ; Social indicators / Mathematical models / Moral and ethical aspects ; Democracy / United States ; United States / Social conditions / 21st century
RSWK: Massendaten / Kritik / Soziale Ungleichheit
BK: 71.52 Kulturelle Prozesse Soziologie ; 54.08 (Informatik in Beziehung zu Mensch und Gesellschaft) ; 71.43 (Technologische Faktoren)
DDC: 005.7 / dc23
SFB: Soz 943
GHBS: OGH (PB)
KAB: E 711
RVK: SR 850 ; ST 530 ; MS 7965
2Bergman, O. ; Whittaker, S.: ¬The science of managing our digital stuff.
Cambridge, MA : MIT Press, 2016. xiii, 275 S.
Abstract: Why we organize our personal digital data the way we do and how design of new PIM systems can help us manage our information more efficiently. Each of us has an ever-growing collection of personal digital data: documents, photographs, PowerPoint presentations, videos, music, emails and texts sent and received. To access any of this, we have to find it. The ease (or difficulty) of finding something depends on how we organize our digital stuff. In this book, personal information management (PIM) experts Ofer Bergman and Steve Whittaker explain why we organize our personal digital data the way we do and how the design of new PIM systems can help us manage our collections more efficiently.
Inhalt: Bergman and Whittaker report that many of us use hierarchical folders for our personal digital organizing. Critics of this method point out that information is hidden from sight in folders that are often within other folders so that we have to remember the exact location of information to access it. Because of this, information scientists suggest other methods: search, more flexible than navigating folders; tags, which allow multiple categorizations; and group information management. Yet Bergman and Whittaker have found in their pioneering PIM research that these other methods that work best for public information management don't work as well for personal information management. Bergman and Whittaker describe personal information collection as curation: we preserve and organize this data to ensure our future access to it. Unlike other information management fields, in PIM the same user organizes and retrieves the information. After explaining the cognitive and psychological reasons that so many prefer folders, Bergman and Whittaker propose the user-subjective approach to PIM, which does not replace folder hierarchies but exploits these unique characteristics of PIM.
Anmerkung: Rez. in: JASIST 68(2017) no.12, S.2834-2840 (William Jones)
Themenfeld: Bibliographische Software
RSWK: Informationsmanagement / Digitalisierung
DDC: 650.1 / dc23
RVK: ST 515 ; ST 530
3Poibeau, T. u.a. (Hrsg.): Multi-source, multilingual information extraction and summarization.
Berlin : Springer, 2013. XX, 323 S.
(Theory and applications of natural language processing)
Abstract: Information extraction (IE) and text summarization (TS) are powerful technologies for finding relevant pieces of information in text and presenting them to the user in condensed form. The ongoing information explosion makes IE and TS critical for successful functioning within the information society. These technologies face particular challenges due to the inherent multi-source nature of the information explosion. The technologies must now handle not isolated texts or individual narratives, but rather large-scale repositories and streams---in general, in multiple languages---containing a multiplicity of perspectives, opinions, or commentaries on particular topics, entities or events. There is thus a need to adapt existing techniques and develop new ones to deal with these challenges. This volume contains a selection of papers that present a variety of methodologies for content identification and extraction, as well as for content fusion and regeneration. The chapters cover various aspects of the challenges, depending on the nature of the information sought---names vs. events,--- and the nature of the sources---news streams vs. image captions vs. scientific research papers, etc. This volume aims to offer a broad and representative sample of studies from this very active research field.
Anmerkung: Rez. in: JASIST 64(2013) no.7, S.1519-1521 (José L. Vicedo, David Tomás)
RSWK: Natürlichsprachiges System / Information Extraction / Automatische Inhaltsanalyse / Zusammenfassung / Aufsatzsammlung
BK: 54.75 (Sprachverarbeitung)
DDC: 006.312 / DDC22ger ; 005.74 / DDC22ger
RVK: ST 530 ; ST 306 ; AN 95300
4Geiselberger, H. u.a. [Red.]: Big Data : das neue Versprechen der Allwissenheit.
Berlin : Suhrkamp, 2013. 309 S.
(Edition Unseld : Sonderdruck)
Abstract: Der Begriff Big Data hat spätestens in diesem Jahr der Überwachung den Durchbruch geschafft - mit dem Sammelband des Suhrkamp Verlags bekommt nun jedermann den Data-Durchblick. ... Experten aus Theorie und Praxis bringen ihre Erfahrungen und Meinungen im Suhrkamp-Werk kurz und präzise auf den Punkt und bieten damit einen guten Überblick über die Thematik, die gerade erst in den Startlöchern steht.
Objekt: PRISM ; Tempora
RSWK: Informationsgesellschaft / Sozialer Wandel / Massendaten / Datenanalyse / Informationsüberlastung / Datenschutz / Aufsatzsammlung ; World Wide Web / Privatsphäre / Datenschutz / Aufsatzsammlung (BVB)
BK: 54.08 Informatik in Beziehung zu Mensch und Gesellschaft ; 54.38 Computersicherheit ; 54.62 Datenstrukturen ; 71.43 Technologische Faktoren Soziologie
DDC: 303.4834 / DDC22ger
GHBS: QGT (DU) ; PZY (E) ; QFD (W) ; TWO (FHK)
RVK: AN 13400 ; ; AP 15900 ; MS 6950 ; MS 7965 ; SR 850 ; ST 530
5Aggarwal, C.C. u. C.X. Zhai (Hrsg.): Mining text data.
New York : Springer, 2012. XI, 522 S.
Abstract: Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.
Inhalt: Inhalt: An Introduction to Text Mining.- Information Extraction from Text.- A Survey of Text Summarization Techniques.- A Survey of Text Clustering Algorithms.- Dimensionality Reduction and Topic Modeling.- A Survey of Text Classification Algorithms.- Transfer Learning for Text Mining.- Probabilistic Models for Text Mining.- Mining Text Streams.- Translingual Mining from Text Data.- Text Mining in Multimedia.- Text Analytics in Social Media.- A Survey of Opinion Mining and Sentiment Analysis.- Biomedical Text Mining: A Survey of Recent Progress.- Index.
Anmerkung: Elektronische Ausgabe unter: http://springer.r.delivery.net/r/r?2.1.Ee.2Tp.1gd0L5.C3WE8i..N.WdtI.3uq2.bW89MQ%5f%5fCXccFOL0.
Themenfeld: Data Mining
LCSH: Computer science ; Computer Communication Networks ; Database management ; Data mining ; Multimedia systems
RSWK: Text Mining / Aufsatzsammlung
RVK: ST 306 ; ST 530
6Liu, B.: Web data mining : exploring hyperlinks, contents, and usage data.2nd ed.
Heidelberg : Springer, 2011. XX, 622 S.
(Data-centric systems and applications)
Abstract: Web mining aims to discover useful information and knowledge from the Web hyperlink structure, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semistructured and unstructured nature of the Web data and its heterogeneity. It has also developed many of its own algorithms and techniques. Liu has written a comprehensive text on Web data mining. Key topics of structure mining, content mining, and usage mining are covered both in breadth and in depth. His book brings together all the essential concepts and algorithms from related areas such as data mining, machine learning, and text processing to form an authoritative and coherent text. The book offers a rich blend of theory and practice, addressing seminal research ideas, as well as examining the technology from a practical point of view. It is suitable for students, researchers and practitioners interested in Web mining both as a learning text and a reference book. Lecturers can readily use it for classes on data mining, Web mining, and Web search. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.
Inhalt: Inhalt: 1. Introduction 2. Association Rules and Sequential Patterns 3. Supervised Learning 4. Unsupervised Learning 5. Partially Supervised Learning 6. Information Retrieval and Web Search 7. Social Network Analysis 8. Web Crawling 9. Structured Data Extraction: Wrapper Generation 10. Information Integration
Anmerkung: Elektronische Ausgabe unter: http://springer.r.delivery.net/r/r?2.1.Ee.2Tp.1gd0L5.C3WE8i..N.WdtE.3uq2.bW89MQ%5f%5fCXPUFOH0.
Themenfeld: Data Mining
RSWK: World Wide Web / Data Mining
BK: 54.72 ; 06.74 ; 06.70 ; 54.32
DDC: 006.312 / DDC22ger ; 005.7402854678 / DDC22ger ; 005.72 / DDC22ger
GHBS: TZG (FH K) ; TWX (FH GE)
RVK: ST 530
7Pang, B. ; Lee, L.: Opinion mining and sentiment analysis.
Boston, MA : Now Publ., 2008. IX, 137 S.
(Foundations and trends(r) in information retrieval; 2,1/2)
Abstract: An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. Opinion Mining and Sentiment Analysis covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. The focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. The survey includes an enumeration of the various applications, a look at general challenges and discusses categorization, extraction and summarization. Finally, it moves beyond just the technical issues, devoting significant attention to the broader implications that the development of opinion-oriented information-access services have: questions of privacy, vulnerability to manipulation, and whether or not reviews can have measurable economic impact. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided. Opinion Mining and Sentiment Analysis is the first such comprehensive survey of this vibrant and important research area and will be of interest to anyone with an interest in opinion-oriented information-seeking systems.
Inhalt: Table of contents 1. Introduction 2. Applications 3. General Challenges 4. Classification and Extraction 5. Summarization 6. Broader Implications 7. Publicly Available Resources 8. Concluding Remarks References
LCSH: Information behavior ; Research ; Information retrieval ; Public opinion ; Text processing (Computer science)
RSWK: World Wide Web / Meinungsäußerung / Data Mining ; Data Mining / Psycholinguistik (BVB)
BK: 54.72 (Künstliche Intelligenz)
RVK: ST 530
8Kantardzic, M.: Data mining : concepts, models, methods, and algorithms.
Hoboken, NJ : Wiley-Interscience, 2003. XII, 345 S.
Abstract: This book offers a comprehensive introduction to the exploding field of data mining. We are surrounded by data, numerical and otherwise, which must be analyzed and processed to convert it into information that informs, instructs, answers, or otherwise aids understanding and decision-making. Due to the ever-increasing complexity and size of today's data sets, a new term, data mining, was created to describe the indirect, automatic data analysis techniques that utilize more complex and sophisticated tools than those which analysts used in the past to do mere data analysis. "Data Mining: Concepts, Models, Methods, and Algorithms" discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples. This text offers guidance: how and when to use a particular software tool (with their companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided. This book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here. Data mining is an exploding field and this book offers much-needed guidance to selecting among the numerous analysis programs that are available.
Themenfeld: Data Mining
LCSH: Data mining
RSWK: Data Mining / Lehrbuch
BK: 06.74 Informationssysteme
DDC: 006.3/12 / dc22
GHBS: TWX (E) ; PZY (FH K)
LCC: QA76.9.D343K36 2003
RVK: ST 270 ; ST 530