Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Mapping of oil and gas exploration business data entities for effective operational management

Spatio-temporal data of petroleum resources businesses are heterogeneous in nature with multiple relationships among various entities and attributes. Object oriented (OO) systems provide alternative solutions for handling the complex exploration business data entities, where traditional database systems pose serious limitations. Exploration, which is a key business object class in any petroleum business environment, is composed of several sub classes, such as navigation, seismic, vertical seismic profiling (VSP), well-log and reservoir. Authors classify these typical spatio-temporal data items as classes and sub class objects in the OO modelling. In the present paper, logical entity relationship (ER) models have been re-written in multidimensional and object-oriented models. Syntax of typical exploration data object classes, attributes, operations and their relationships has been described for implementation purposes. This work demonstrates how object class logical data models are flexible and interoperable for fast changing petroleum business situations. Models presented in this paper, guide exploration data managers for effectively managing their operations. An OLAP model discussed in this paper is a pursuit of cost saving detailed exploration for oil and gas prospect investigation in any basin

Identity And Privacy Services

Personal identity and privacy are important topics in information systems in general and data analytics in particular.  Normally associated with digital security, the scope of identity and privacy is much greater and affects most aspects of everyday life.  Related subjects are behavioral tracking, personal-identifiable information (PII), privacy data relevance, data repurposing, identity theft, and homeland security.  Identity and Privacy Services is an admixture of the major issues in the area of personal identity and privacy and the security of individual rights in a complex societal environment.  This is a general paper on this important subject, intended to give exposure to the constituent topics

Towards bioinformatics assisted infectious disease control

BACKGROUND: This paper proposes a novel framework for bioinformatics assisted biosurveillance and early warning to address the inefficiencies in traditional surveillance as well as the need for more timely and comprehensive infection monitoring and control. It leverages on breakthroughs in rapid, high-throughput molecular profiling of microorganisms and text mining. RESULTS: This framework combines the genetic and geographic data of a pathogen to reconstruct its history and to identify the migration routes through which the strains spread regionally and internationally. A pilot study of Salmonella typhimurium genotype clustering and temporospatial outbreak analysis demonstrated better discrimination power than traditional phage typing. Half of the outbreaks were detected in the first half of their duration. CONCLUSION: The microbial profiling and biosurveillance focused text mining tools can enable integrated infectious disease outbreak detection and response environments based upon bioinformatics knowledge models and measured by outcomes including the accuracy and timeliness of outbreak detection.9 page(s