Blondel, V. D.; Decuyper, A. & Krings, G.: A survey of results on mobile phone datasets analysis. , 2015
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In this paper, we review some advances made recently in the study of mobile
one datasets. This area of research has emerged a decade ago, with the
creasing availability of large-scale anonymized datasets, and has grown into
stand-alone topic. We will survey the contributions made so far on the social
tworks that can be constructed with such data, the study of personal
bility, geographical partitioning, urban planning, and help towards
velopment as well as security and privacy issues.

Piskorski, J. & Yangarber, R.: Information Extraction: Past, Present and Future. In: Poibeau, T.; Saggion, H.; Piskorski, J. & Yangarber, R. (Hrsg.): Multi-source, Multilingual Information Extraction and Summarization. Springer Berlin Heidelberg, 2013Theory and Applications of Natural Language Processing , S. 23-49
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In this chapter we present a brief overview of Information Extraction, which is an area of natural language processing that deals with finding factual information in free text. In formal terms,

Lü, L.; Medo, M.; Yeung, C. H.; Zhang, Y.-C.; Zhang, Z.-K. & Zhou, T.: Recommender systems. In: Physics Reports 519 (2012), Nr. 1, S. 1 - 49
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The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has great scientific depth and combines diverse research fields which makes it interesting for physicists as well as interdisciplinary researchers.

Christin, D.; Reinhardt, A.; Kanhere, S. S. & Hollick, M.: A survey on privacy in mobile participatory sensing applications. In: Journal of Systems and Software 84 (2011), Nr. 11, S. 1928-1946
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Collobert, R.; Weston, J.; Bottou, L.; Karlen, M.; Kavukcuoglu, K. & Kuksa, P. P.: Natural Language Processing (almost) from Scratch. In: CoRR abs/1103.0398 (2011),
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Ricci, F.; Rokach, L.; Shapira, B. & Kantor, P. B. (Hrsg.): Recommender Systems Handbook. Recommender Systems Handbook. Springer, 2011
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Herrera, F.; Carmona, C.; González, P. & del Jesus, M.: An overview on subgroup discovery: foundations and applications. In: Knowledge and Information Systems (2010), S. 1-31
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Subgroup discovery is a data mining technique which extracts interesting rules with respect to a target variable. An important characteristic of this task is the combination of predictive and descriptive induction. An overview related to the task of subgroup discovery is presented. This review focuses on the foundations, algorithms, and advanced studies together with the applications of subgroup discovery presented throughout the specialised bibliography.

Lane, N.; Miluzzo, E.; Lu, H.; Peebles, D.; Choudhury, T. & Campbell, A.: A survey of mobile phone sensing. In: Communications Magazine, IEEE 48 (2010), Nr. 9, S. 140-150

Liu, B.: Sentiment Analysis and Subjectivity. In: Handbook of Natural Language Processing 2nd ed (2010),

Alonso, S.; Cabrerizo, F.; Herrera-Viedma, E. & Herrera, F.: h-Index: A review focused in its variants, computation and standardization for different scientific fields . In: Journal of Informetrics 3 (2009), Nr. 4, S. 273 - 289
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Goldenberg, A.; Zheng, A. X.; Fienberg, S. E. & Airoldi, E. M.: A survey of statistical network models. , 2009
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Networks are ubiquitous in science and have become a focal point for
scussion in everyday life. Formal statistical models for the analysis of
twork data have emerged as a major topic of interest in diverse areas of
udy, and most of these involve a form of graphical representation.
obability models on graphs date back to 1959. Along with empirical studies in
cial psychology and sociology from the 1960s, these early works generated an
tive network community and a substantial literature in the 1970s. This effort
ved into the statistical literature in the late 1970s and 1980s, and the past
cade has seen a burgeoning network literature in statistical physics and
mputer science. The growth of the World Wide Web and the emergence of online
tworking communities such as Facebook, MySpace, and LinkedIn, and a host of
re specialized professional network communities has intensified interest in
e study of networks and network data. Our goal in this review is to provide
e reader with an entry point to this burgeoning literature. We begin with an
erview of the historical development of statistical network modeling and then
introduce a number of examples that have been studied in the network
terature. Our subsequent discussion focuses on a number of prominent static
d dynamic network models and their interconnections. We emphasize formal
del descriptions, and pay special attention to the interpretation of
rameters and their estimation. We end with a description of some open
oblems and challenges for machine learning and statistics.

Gunawardana, A. & Shani, G.: A Survey of Accuracy Evaluation Metrics of Recommendation Tasks . 2935 , 2009
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Kolda, T. G. & Bader, B. W.: Tensor Decompositions and Applications.. In: SIAM Review 51 (2009), Nr. 3, S. 455-500
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Staab, S. & Studer, R.: Handbook on ontologies. Berlin: Springer, 2009
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An ontology is a formal description of concepts and relationships that can exist for a community of human and/or machine agents. This book considers ontology languages, ontology engineering methods, example ontologies, infrastructures and technologies for ontologies, and how to bring this all into ontology-based infrastructures and applications.

Stamatatos, E.: A survey of modern authorship attribution methods. In: Journal of the American Society for Information Science and Technology 60 (2009), Nr. 3, S. 538-556
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Authorship attribution supported by statistical or computational methods has a long history starting from the 19th century and is marked by the seminal study of Mosteller and Wallace (1964) on the authorship of the disputed “Federalist Papers.” During the last decade, this scientific field has been developed substantially, taking advantage of research advances in areas such as machine learning, information retrieval, and natural language processing. The plethora of available electronic texts (e.g., e-mail messages, online forum messages, blogs, source code, etc.) indicates a wide variety of applications of this technology, provided it is able to handle short and noisy text from multiple candidate authors. In this article, a survey of recent advances of the automated approaches to attributing authorship is presented, examining their characteristics for both text representation and text classification. The focus of this survey is on computational requirements and settings rather than on linguistic or literary issues. We also discuss evaluation methodologies and criteria for authorship attribution studies and list open questions that will attract future work in this area.

Vinciarelli, A.; Pantic, M. & Bourlard, H.: Social signal processing: Survey of an emerging domain. In: Image and Vision Computing 27 (2009), Nr. 12, S. 1743-1759
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The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for social signal processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially aware computing.

Cimiano, P.; V"olker, J. & Studer, R.: Ontologies on Demand? -A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text Information. In: Information, Wissenschaft und Praxis 57 (2006), Nr. 6-7, S. 315-320
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Berkhin, P.: A survey on pagerank computing. In: Internet Mathematics 2 (2005), S. 73-120
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Abstract. This survey reviews the research related to PageRank computing. Components of a PageRank vector serve as authority weights for web pages independent of their textual content, solely based on the hyperlink structure of the web. PageRank is typically used as a web search ranking component. This defines the importance of the model and the data structures that underly PageRank processing. Computing even a single PageRank is a difficult computational task. Computing many PageRanks is a much more complex challenge. Recently, significant effort has been invested in building sets of personalized PageRank vectors. PageRank is also used in many diverse applications other than ranking. We are interested in the theoretical foundations of the PageRank formulation, in the acceleration of PageRank computing, in the effects of particular aspects of web graph structure on the optimal organization of computations, and in PageRank stability. We also review alternative models that lead to authority indices similar to PageRank and the role of such indices in applications other than web search. We also discuss linkbased search personalization and outline some aspects of PageRank infrastructure from associated measures of convergence to link preprocessing. 1.

Brank, J.; Grobelnik, M. & Mladenić, D.: A Survey of Ontology Evaluation Techniques. Proc. of 8th Int. multi-conf. Information Society. 2005, S. 166-169

Gómez-Pérez, A. & Manzano-Macho, D.: A survey of ontology learning methods and techniques. , 2003
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