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  • 1.
    Fakas, Georgios J.
    et al.
    Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.
    Zhi, Cai
    College of Computer Science Beijing University of Technology, Beijing, China.
    Mamoulis, Nikos
    Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong.
    Diverse and proportional size-l object summaries using pairwise relevance2016Ingår i: The VLDB journal, ISSN 1066-8888, E-ISSN 0949-877X, Vol. 25, nr 6, 791-816 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The abundance and ubiquity of graphs (e.g., online social networks such as Google+" style="position: relative;" tabindex="0" id="MathJax-Element-1-Frame" class="MathJax">+ and Facebook; bibliographic graphs such as DBLP) necessitates the effective and efficient search over them. Given a set of keywords that can identify a data subject (DS), a recently proposed keyword search paradigm produces a set of object summaries (OSs) as results. An OS is a tree structure rooted at the DS node (i.e., a node containing the keywords) with surrounding nodes that summarize all data held on the graph about the DS. OS snippets, denoted as size-l OSs, have also been investigated. A size-l OS is a partial OS containing l nodes such that the summation of their importance scores results in the maximum possible total score. However, the set of nodes that maximize the total importance score may result in an uninformative size-l OSs, as very important nodes may be repeated in it, dominating other representative information. In view of this limitation, in this paper, we investigate the effective and efficient generation of two novel types of OS snippets, i.e., diverse and proportional size-l OSs, denoted as DSize-l and PSize-l OSs. Namely, besides the importance of each node, we also consider its pairwise relevance (similarity) to the other nodes in the OS and the snippet. We conduct an extensive evaluation on two real graphs (DBLP and Google+" style="position: relative;" tabindex="0" id="MathJax-Element-2-Frame" class="MathJax">+). We verify effectiveness by collecting user feedback, e.g., by asking DBLP authors (i.e., the DSs themselves) to evaluate our results. In addition, we verify the efficiency of our algorithms and evaluate the quality of the snippets that they produce.

  • 2. Kotsifakos, Alexios
    et al.
    Karlsson, Isak
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Papapetrou, Panagiotis
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Athitsos, Vassilis
    Gunopulos, Dimitrios
    Embedding-based subsequence matching with gaps-range-tolerances: a Query-By-Humming application2015Ingår i: The VLDB journal, ISSN 1066-8888, E-ISSN 0949-877X, Vol. 24, nr 4, 519-536 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We present a subsequence matching framework that allows for gaps in both query and target sequences, employs variable matching tolerance efficiently tuned for each query and target sequence, and constrains the maximum matching range. Using this framework, a dynamic programming method is proposed, called SMBGT, that, given a short query sequence Q and a large database, identifies in quadratic time the subsequence of the database that best matches Q. SMBGT is highly applicable to music retrieval. However, in Query-By-Humming applications, runtime is critical. Hence, we propose a novel embedding-based approach, called ISMBGT, for speeding up search under SMBGT. Using a set of reference sequences, ISMBGT maps both Q and each position of each database sequence into vectors. The database vectors closest to the query vector are identified, and SMBGT is then applied between Q and the subsequences that correspond to those database vectors. The key novelties of ISMBGT are that it does not require training, it is query sensitive, and it exploits the flexibility of SMBGT. We present an extensive experimental evaluation using synthetic and hummed queries on a large music database. Our findings show that ISMBGT can achieve speedups of up to an order of magnitude against brute-force search and over an order of magnitude against cDTW, while maintaining a retrieval accuracy very close to that of brute-force search.

  • 3.
    Magnani, Matteo
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datalogi.
    Assent, Ira
    Mortensen, Michael L.
    Taking the Big Picture: Representative Skylines based on Significance and Diversity2014Ingår i: The VLDB journal, ISSN 1066-8888, E-ISSN 0949-877X, Vol. 23, nr 5, 795-815 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The skyline is a popular operator to extract records from a database when a record scoring function is not available. However, the result of a skyline query can be very large. The problem addressed in this paper is the automatic selection of a small number of representative skyline records. Existing approaches have only focused on partial aspects of this problem. Some try to identify sets of diverse records giving an overall approximation of the skyline. These techniques, however, are sensitive to the scaling of attributes or to the insertion of non-skyline records into the database. Others exploit some knowledge of the record scoring function to identify the most significant record, but not sets of records representative of the whole skyline. In this paper, we introduce a novel approach taking both the significance of all the records and their diversity into account, adapting to available knowledge of the scoring function, but also working under complete ignorance. We show the intractability of the problem and present approximate algorithms. We experimentally show that our approach is efficient, scalable and that it improves existing works in terms of the significance and diversity of the results.

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