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We present a configurable, hybrid, and parallel covering array constructor, called CHiP. CHiP is parallel in that it utilizes vast amount of parallelism provided by graphics processing units (GPUs). CHiP is hybrid in that it bundles the bests of two construction approaches for computing covering arrays; a metaheuristic search-based approach for efficiently covering a large portion of the required combinations and a constraint satisfaction-based approach for effectively covering the remaining hard-to-cover-by-chance combinations. CHiP is configurable in that a trade-off between covering array sizes and construction times can be made. We have conducted a series of experiments, in which we compared the efficiency and effectiveness of CHiP to those of a number of existing constructors by using both full factorial designs and well-known benchmarks. In these experiments, we report new upper bounds on covering array sizes, demonstrating the effectiveness of CHiP, and the first results for a higher coverage strength, demonstrating the scalability of CHiP.

Literature search is one of the most important steps of academic research. With more than 100,000 papers published each year just in computer science, performing a complete literature search becomes a Herculean task. Some of the existing approaches and tools for literature search cannot compete with the characteristics of today’s literature, and they suffer from ambiguity and homonymy. Techniques based on citation information are more robust to the mentioned issues. Thus, we recently built a Web service called the advisor, which provides personalized recommendations to researchers based on their papers of interest. Since most recommendation methods may return redundant results, diversifying the results of the search process is necessary to increase the amount of information that one can reach via an automated search. This article targets the problem of result diversification in citation-based bibliographic search, assuming that the citation graph itself is the only information available and no categories or intents are known. The contribution of this work is threefold. We survey various random walk--based diversification methods and enhance them with the direction awarenessproperty to allow users to reach either old, foundational (possibly well-cited and well-known) research papers or recent (most likely less-known) ones. Next, we propose a set of novel algorithms based on vertex selection and query refinement. A set of experiments with various evaluation criteria shows that the proposed γ-RLM algorithm performs better than the existing approaches and is suitable for real-time bibliographic search in practice.

Graphs and matrices are widely used in algorithms for social network analyses. Since the number of interactions is much less than the possible number of interactions, the graphs and matrices used in the analyses are usually sparse. In this paper, we propose an efficient implementation of a sparse-matrix computation which arises in our publicly available citation recommendation service theadvisoras well as in many other recommendation systems. The recommendation algorithm uses a sparse matrix generated from the citation graph. We observed that the nonzero pattern of this matrix is highly irregular and the computation suffers from high number of cache misses. We propose techniques for storing the matrix in memory efficiently and we reduced the number of cache misses with ordering and partitioning. Experimental results show that our techniques are highly efficient in reducing the query processing time which is highly crucial for a web service.

The ability to summarize a large number of network patterns discovered from biomedical data provides valuable information for use in many applications. We show that several variants of the problem are all NP-hard, and merging network patterns is a practical solution for these applications. In this work, we propose an algorithmic framework for merging network patterns. We have developed fast algorithms under this general framework which supports several types of biomedical network data. In addition, our empirical study demonstrates that our algorithms are efficient in merging a large number of biomedical network patterns and can be configured for various knowledge discovery purposes.The ability to summarize a large number of network patterns discovered from biomedical data provides valuable information for use in many applications. We show that several variants of the problem are all NP-hard, and merging network patterns is a practical solution for these applications. In this work, we propose an algorithmic framework for merging network patterns. We have developed fast algorithms under this general framework which supports several types of biomedical network data. In addition, our empirical study demonstrates that our algorithms are efficient in merging a large number of biomedical network patterns and can be configured for various knowledge discovery purposes.