101,977 research outputs found
Recent advances in mining patterns from complex data
No full textData mining and knowledge discovery are advanced research fields with numerous algorithms and studies to extract patterns and models from complex data sources like blogs, event or log data, biological data, spatio-temporal data, social networks, mobility data, and sensor data and streams. The works presented in this special issue of the Journal of Intelligent Information Systems should keep the attention of both researchers and practitioners of data mining who are interested in the advances and latest developments in the area of extracting patterns. Behavioral Process Mining for Unstructured Processes by Claudia Diamantini, Laura Genga and Domenico Potena addresses the challenging problem of extracting useful information from the huge volume of events recorded by several of today's enterprise systems
A Relational Unsupervised Approach to Author Identification
No full textIn the last decades speaking and writing habits have changed.
Many works faced the author identification task by exploiting frequencybased
approaches, numeric techniques or writing style analysis. Following
the last approach we propose a technique for author identification
based on First-Order Logic. Specifically, we translate the complex data
represented by natural language text to complex (relational) patterns
that represent the writing style of an author. Then, we model an author
as the result of clustering the relational descriptions associated to the
sentences. The underlying idea is that such a model can express the typical
way in which an author composes the sentences in his writings. So,
if we can map such writing habits from the unknown-author model to
the known-author model, we can conclude that the author is the same.
Preliminary results are promising and the approach seems viable in real
contexts since it does not need a training phase and performs well also
with short texts
A survey of Big Data dimensions vs Social Networks analysis
No full textThe pervasive diffusion of Social Networks (SN) produced an unprecedented amount of heterogeneous data. Thus, traditional approaches quickly became unpractical for real life applications due their intrinsic properties: large amount of user-generated data (text, video, image and audio), data heterogeneity and high speed generation rate. More in detail, the analysis of user generated data by popular social networks (i.e Facebook (https://www.facebook.com/), Twitter (https://www.twitter.com/), Instagram (https://www.instagram.com/), LinkedIn (https://www.linkedin.com/)) poses quite intriguing challenges for both research and industry communities in the task of analyzing user behavior, user interactions, link evolution, opinion spreading and several other important aspects. This survey will focus on the analyses performed in last two decades on these kind of data w.r.t. the dimensions defined for Big Data paradigm (the so called Big Data 6 V’s)
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