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As the number of people who use scientific literature databases has grown, the demand for literature retrieval services has steadily increased. One of the most popular retrieval service methods is to find a set of papers similar to the paper under consideration, which requires a measure that computes the similarities between the papers. Scientific literature databases exhibit two interesting characteristics that are not found in general databases. First, the papers cited by older papers are often not included in the database due to technical and economic reasons. Second, since a paper references previously published papers, few papers cite recently published papers. These two characteristics cause all existing similarity measures to fail in at least one of the following cases: (1) measuring the similarity between old, but similar papers, (2) measuring the similarity between recent, but similar papers, and (3) measuring the similarity between two similar papers: one old, the other recent. In this paper, we propose a new link-based similarity measure called C-Rank, which uses both in-link and out-link references, disregarding the direction of the references. In addition, we discuss the most suitable normalization method for scientific literature databases and we propose an evaluation method for measuring the accuracy of similarity measures. For the experiments, we used real-world papers from DBLP's database with reference information crawled from Libra. We then compared the performance of C-Rank with that of existing similarity measures. Experimental results showed that C-Rank achieved a higher accuracy than existing similarity measures. [ABSTRACT FROM AUTHOR]

A classic t -private PIR (private information retrieval) scheme allows a user to retrieve one out of n values from k communicating replicated databases while any t of the k databases cannot identify the value being retrieved. However, in reality, the user may be more interested in retrieving multiple values simultaneously. This paper is devoted to the PIR problem with m retrieved values and k databases against t colluding databases (t -private MPIR) in the context of information-theoretic security. The relationship m ⩽ w (k - t) is derived, where w is the largest average amount of information got by the user from each of the k databases. When w = 1 , via trace functions in finite fields, a t -private MPIR scheme retrieving m = k - t values is presented with the complexity O (log n). Our scheme provides a lower complexity than those in the literature (the best known complexity for general t and k is a fractional power function of n). [ABSTRACT FROM AUTHOR]

Abstract: Retrieving representative information from large-scale data becomes an important research issue nowadays, especially in the context of mobile business/search where the screen size and navigability are limited. This paper focuses on certain aspects of representativeness in database queries and web search, and proposes an approach to extracting a subset of results from original search results in light of high coverage and low redundancy. In the paper, the notion of λ-represent is introduced, which enables us to describe the λ-represent relationship between the sets of data objects. Then, the λ-representative problem is formulated as an extension of the typical set covering problem, which leads to developing a heuristic approach (namely, LamRep) to coping with the problem effectively and efficiently. Notably, LamRep is incorporated with a “vote” mechanism, enhanced with an algorithmic acceleration strategy. Data experiments on benchmark data and a real-world example show that LamRep outperforms the other approaches. [Copyright &y& Elsevier]

Abstract: Cloud computing emerges as a new computing paradigm that aims to provide reliable, customized and quality of service guaranteed computation environments for cloud users. Applications and databases are moved to the large centralized data centers, called cloud. Due to resource virtualization, global replication and migration, the physical absence of data and machine in the cloud, the stored data in the cloud and the computation results may not be well managed and fully trusted by the cloud users. Most of the previous work on the cloud security focuses on the storage security rather than taking the computation security into consideration together. In this paper, we propose a privacy cheating discouragement and secure computation auditing protocol, or SecCloud, which is a first protocol bridging secure storage and secure computation auditing in cloud and achieving privacy cheating discouragement by designated verifier signature, batch verification and probabilistic sampling techniques. The detailed analysis is given to obtain an optimal sampling size to minimize the cost. Another major contribution of this paper is that we build a practical secure-aware cloud computing experimental environment, or SecHDFS, as a test bed to implement SecCloud. Further experimental results have demonstrated the effectiveness and efficiency of the proposed SecCloud. [Copyright &y& Elsevier]

Abstract: In many example-based information retrieval tasks, example query actually contains multiple sub-queries. For example, in 3D object retrieval, the query is an object described by multiple views. In content-based video retrieval, the query is a video clip that contains multiple frames. Without prior knowledge, the most intuitive approach is to treat the sub-queries equally without difference. In this paper, we propose a k-partite graph reinforcement approach to fuse these sub-queries based on the to-be-retrieved database. The approach first collects the top retrieved results. These results are regarded as pseudo-relevant samples and then a k-partite graph reinforcement is performed on these samples and the query. In the reinforcement process, the weights of the sub-queries are updated by an iterative process. We present experiments on 3D object retrieval and content-based video clip retrieval, and the results demonstrate that our method effectively boosts retrieval performance. [Copyright &y& Elsevier]

Abstract: In this paper, we discuss similarity searches for time series data represented as interval sequences. For instance, the time series of phone call records can be represented by time-based interval sequences, or -interval sequences, which consist of the start and end times of the call records. To support an efficient similarity search for such sequences, we address the desirable semantics for similarity measures for the -interval sequences, observe how existing measures fail to address such semantics, and propose a new measure that satisfies all our semantics. We then propose approximate encoding methods for -interval sequences. More specifically, we propose two bitmap-based feature extraction methods: (1) a bin-bitmap encoding method that transforms the -interval sequences into bitmaps of fixed length, and (2) a segmented feature extraction method that takes the longest bitmap sequences of consecutive ‘1’ elements. Finally, we propose two query processing schemes using these bitmap-based approximate representations. We validate the efficiency and effectiveness of our proposed solutions empirically. [Copyright &y& Elsevier]

Abstract: Skyline queries return a set of objects, or a skyline, that are not dominated by any other objects. While providing users with an intuitive query formulation, the skyline queries may incur too many results, especially, for high dimensional data. To tackle this problem, subspace skyline queries, which deals with a subset of dimensions, have been recently studied. To identify interesting skylines, users can iteratively refine multiple relevant subspaces for skyline queries. Existing work focuses primarily on supporting efficient subspace skyline computation in centralized databases. In clear contrast, this paper aims to address subspace skyline computation in distributed environments such as the Web. Toward this goal, we make use of pre-computed subspace skylines as views in databases, called skyline views. Specifically, we propose distributed subspace skyline computation which minimizes the total access cost by leveraging the skyline views. Our experimental results validate that our proposed algorithms significantly outperform state-of-the-art algorithms in extensive synthetic datasets. [Copyright &y& Elsevier]

Abstract: This article concerns the definition of linguistic summaries of data in the relational model of databases. Among the different approaches, the propositions calling on linguistic quantifiers are very appealing since they offer flexibility and a linguistic formulation which can be easily understood by a user. More precisely, these linguistic summaries are made of a quantified statement associated with a degree of validity (computed from the database). Such a summary shows that a quantity of information (quantity described by the linguistic quantifier) satisfies a constraint (defined by a fuzzy predicate). This article shows that the already proposed approaches do not sufficiently stress the relationship between these two aspects. As a consequence, we propose another kind of linguistic summaries made of a linguistic statement associated with a degree of validity having a clear meaning in terms of quantity and quality. It becomes possible to compare two linguistic summaries and to understand the difference in their degrees of validity. These summaries are called functional linguistic summaries because their interpretation relies on a particular set-oriented function. They present the advantages of using fuzzy terms to reflect that the user does not exactly know what can be expected from the database. However, the degree of validity provides a precise description of the stored information. In other words, starting from a rough idea of what can be expected as a summary, the obtained degree of truth provides a precise description of the information stored in the database. The relationship of the proposed linguistic summaries with the quantified statements are studied in this paper. It turns out that these linguistic summaries can be viewed as particular quantified statements having particular properties. [Copyright &y& Elsevier]

Abstract: A novel modeling method for a collection of short text snippets is presented in this paper to measure the similarity between pairs of snippets. The method takes account of both the semantic and statistical information within the short text snippets, and consists of three steps. Given a set of raw short text snippets, it first establishes the initial similarity between words by using a lexical database. The method then iteratively calculates both word similarity and short text similarity. Finally, a proximity matrix is constructed based on word similarity and used to convert the raw text snippets into vectors. Word similarity and text clustering experiments show that the proposed short text modeling method improves the performance of existing text-related information retrieval (IR) techniques. [ABSTRACT FROM AUTHOR]

Abstract: The management of large collections of music data in a multimedia database has received much attention in the past few years. In the majority of current work, researchers extract the features, such as melodies, rhythms, and chords, from the music data and develop indices that will help to retrieve the relevant music quickly. Several reports have pointed out that these music features can be transformed and represented in forms of music feature strings or numeric values so that indices can be created for music retrieval. However, there are only a small number of existing approaches which introduce multi-feature index structures for music queries while most of the others are for developing single feature indices. The existing music multi-feature index structures are memory consuming and have lack of scalability. In this paper, we will propose a two-tier music index structure which is an efficient and scalable approach for multi-feature music indexing. Our experimental results show that this new approach outperforms existing multi-feature index schemes. [Copyright &y& Elsevier]

The factors that affect the performance of mobile agents in retrieving information from the Internet are the number of agents and the total of routing time taken by the participated agents to complete the assigned tasks. Fewer numbers of mobile agents used to execute the tasks will cause lower network traffic and consume less bandwidth, and the total time taken to retrieve the query results can be minimized. In this paper, the performance of mobile agents for query retrieval is analyzed. Specifically, the performance of mobile agent in obtaining a query result from the remote hosts using an extended hierarchical query retrieval (EHQR) approach is proposed. It is based on a hierarchical and a parallel dispatching of mobile agents to the remote servers in order to retrieve the query results. Experimental results show that the proposed approach reduces the number of mobile agents and also improve the total time taken to retrieve the query results compared with other approaches. [ABSTRACT FROM AUTHOR]