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  • Graph Databases Lifecycle Methodology and Tool, RDF building, indexing and versioning

    Graph databases are taking place in many different applications: smart city, smart cloud, smart education, etc. In most cases, the applications imply the creation of ontologies and the integration of a large set of knowledge to build a knowledge base as an RDF KB store, with ontologies, static data, historical data and real time data. Most of the RDF stores are endowed of inferential engines that materialize some knowledge as triples during indexing or querying. In these cases, the delete of concepts may imply the removal and change of many triples, especially if the triples are those modeling the ontological part of the knowledge base, or are referred by many other concepts. For these solutions, the graph database versioning feature is not provided at level of the RDF stores tool, and it is quite complex and time consuming to be addressed as black box approach. In most cases the indexing is time consuming process, and the rebuilding of the KB may imply manually edited long scripts that are error prone. Therefore, in order to solve these kinds of problems, a lifecycle methodology and a tool supporting versioning of indexes for RDF KB store is proposed. The solution proposed has been developed on the basis of a number of knowledge oriented projects as Sii-Mobility (smart city), RESOLUTE (smart city risk assessment), ICARO (smart cloud). Results are reported in terms of time saving and reliability.


    Figure 1.   RDF Index Buidling Monitor
     

    For the RDF Index generation the RDF Index Manager produces a script according to the index descriptor and the RDF store target. The script is structured in the following steps: (i) setup of script, (ii) initialization of RDF store, (iii) bulk uploading of triples into the store, (iv) RDF store finalization, (v) create eventual additional indexes as textual indexes, geographical indexes that need additional database commands, and (vi) update index building status. In most cases, the RDF store rebuilt by indexing is time consuming, and may imply manually edited long scripts that are error prone. In order to solve this kind of problem, in this paper, a lifecycle methodology and our RIM tool for RDF KB store versioning is proposed. The results have shown that saving time up to the  95% depending on the number of triples, files and cases to be indexed.
     

    Pubblicato da root il Dom, 2015-06-07 10:57
  • Talk at DISIT: Inferring Personal Traits from Social Media, Dongwon Lee, Pennsylvania State University, USA.

    22 maggio, ore 11:30, presso auletta del dipartimento di ingegneria dell'informazione, via S. Marta, 3, Firenze, sarà tenuto il seguente seminario dal Prof. Dongwon Lee, della Pennsylvania State University, USA.
     

    Inferring Personal Traits from Social Media

    Dongwon Lee, Pennsylvania State University, USA.

    Abstract: Social Network Services (SNSs) have become an integral part of people¢s lives in recent years, facilitating the social relations among people who share similar activities or interests in online and/or offline lives. With an explosively growing number of users, SNSs produce diverse forms of user-generated contents (e.g., tweets, wall posts, uploaded photos, comments) as well as network features (e.g., follower or friend list). One of interesting questions (with practical implications) is to see if one can accurately infer the personal traits of individuals in SNSs using diverse forms of social media data therein. In this talk, I will present a few recent such attempts developed in the SUM project (http://goo.gl/7LsfAJ) at Penn State.

    Bio: Dongwon Lee is an associate professor in the College of Information Sciences and Technology (a.k.a. iSchool) of the Pennsylvania State University, USA. Since Jan. 2015, he has been also serving as a rotating program director at National Science Foundation (NSF). He obtained his Ph.D. in Computer Science from UCLA in 2002. From 1995 to 1997, he has also worked at AT&T Bell Labs.  With the research interests in data science, social computing, and human computation, he has (co-)authored over 130+ scholarly articles that appeared in selective publication outlets. Further details of his research can be found at: http://pike.psu.edu/dongwon/

    Pubblicato da root il Ven, 2015-05-15 09:59
  • DISIT in the Top 5% of Most-Viewed on SlideShare

    Congrats, ...! You are in the Top 5% of Most-Viewed on SlideShare

    Title 2014 Views
    08 Jun, 14
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    05 Mar, 14
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    Pubblicato da root il Mer, 2015-02-18 11:41
  • news on FODD2015: Florence Open Data Day, 2015

    FODD- Firenze Open Data Day
    21/02/2015
    SALONE DE’ DUGENTO – Palazzo Vecchio
    Piazza della Signoria, 1 Firenze

    http://www.disit.org/fodd
     
    Per motivi di sicurezza, la partecipazione all’evento è limitata, si prega di iscriversi rapidamente al seguente link Eventbrite:

    REGISTRATI gratis... via EventBrite


     

    Pubblicato da root il Gio, 2015-02-12 15:26
  • Auguri Natale 2014

    Buon Natale 2014 e felice anno nuovo, da parte del DISIT!
    Pubblicato da root il Mer, 2014-12-24 10:55