Diversifying big data in parallel (Master thesis)
A characteristic that makes a query result distinctive is apparently quality. One way of enhancing the quality of a query result is diversification. Many ways have been already proposed in order to diversify a query result. In this study we utilize the algorithm of Dissimilarity and Coverage as well as the Max Cover algorithm in an attempt to diversify a query result, which is substituted by several sets of data. The novelty that is introduced in this study is the perspective through which we approach those algorithms. Diversification is conducted with the use of parallel implementations of the aforementioned algorithms in a distributed environment. With this thesis, we intend to propose a new way of diversifying Big Data efficiently, taking at the same time advantage of new distributed platforms.
|Institution and School/Department of submitter:||Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Η/Υ & Πληροφορικής|
|Keywords:||Κατανεμημένα συστήματα,Παράλληλη υλοποίηση αλγορίθμων,Ποικιλομορφία,Hadoap,Map-reduce,Diversification,Disc|
|Appears in Collections:||Διατριβές Μεταπτυχιακής Έρευνας (Masters)|
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|Μ.Ε. ΝΟΥΛΗΣ ΚΩΝΣΤΑΝΤΙΝΟΣ 2017.pdf||8.76 MB||Adobe PDF||View/Open|
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