Search

As the basis of many algorithms,the breadth-first search(BFS) algorithm for big graph data has attracted more and more interests industrially and academically.Among the researches on BFS for the big graph,the solid state disk(SSD) is utilized to improve algorithm performance.In the graph traversal,the storage devices are required to continuously and repeatedly load data to fulfill the demand of graph traversal.However,many operations of data wipe caused by continuously and repeatedly loading data severely degrade the lifetime of SSD.For this reason,the lifetime of SSD can be effectively extended by reducing the data for write operations.Firstly,combined with the characteristics of graph structure,the data reuse model is constructed for describing the levels of data reuse in graph traversal.Then,a heuristic priority access algorithm based on vertex degree is proposed.By judging the independence of vertexes,the proposed algorithm selects graph vertexes that are priority accessed for improving the probability of data reusing,increasing hit ratio,and reducing the wear of flash memory.This method is available to any BFS algorithms and datasets without modifying BFS algorithm and big graph data.In the end,simulation results demonstrate that the proposed model and algorithm are effective.By applying the proposed algorithm on three common BFS algorithms:BFS-4K,B40C,and Gunrock,it can effectively reduce the operations of data written in graph traversal and improve the lifetime of SSD 12%,15%,22%,respectively.

Companies and research institutes are acquiring monopolized technologies through patents to survive in a rapidly changing market. The applicant is guaranteed by law the exclusive right to the technology for a certain period. A prior art search is an essential task for this purpose. Therefore, the need for a successful prior art search is increasing. Previous studies have disadvantages because relevant fields are limited, requiring an in-depth understanding of specific technologies. This paper proposes a method to overcome these limitations using patent text and citation information. The proposed method searches for prior art using a citation network based on the shortest path derived from a breadth-first search. We examined based on experiments whether the proposed method could be applied to the industry. Based on the results, the proposed algorithm can efficiently search for prior art. Furthermore, it was possible to construct a counterstrategy based on the characteristics of the applicant. The code will be made available at github.com/lee-ju/k_step_PIC.

With a huge amount of information being stored as structured data, there is an increasing need for retrieving exact answers to questions from tables. Answering natural language questions on structured data usually involves semantic parsing of query to a machine understandable format which is then used to retrieve information from the database. Training semantic parsers for domain specific tasks is a tedious job and does not guarantee accurate results. In this paper, we used conversational analytics tool to create the user interface and to get the required entities and intents from the query thus avoiding the traditional semantic parsing approach. We then make use of Knowledge Graph for querying in structured data domain. Knowledge graphs can be easily leveraged for question answering systems, to use them as the database. We extract appropriate answers for different types of queries which have been illustrated in the Results section.

A key property of linked data, i.e., the web-based representation and publication of data as interconnected labeled graphs, is that it enables querying and navigating through datasets distributed across the network. SPARQL1.1, the current standard query language for RDF-based linked data, defines a construct-called property paths (PP)-to navigate between the entities of a graph. This is potentially very useful in a number of use cases, e.g., in the biomedical domain, where large datasets are available as linked data graphs. However, the use of PP in SPARQL 1.1. is possible only on a single local graph, requiring us to merge all distributed datasets into one large, centrally stored graph, therefore reducing the value of using linked data in the first place. We propose an index-based approach-called QPPDs-for answering queries for paths distributed across multiple, distributed datasets. We provide a heuristic-based source selection mechanism to select the relevant datasets (also called data sources) for a given path query, and a technique that federates queries to selected sources, and assembles (merges) the paths (i.e., partial or complete) retrieved from those remote datasets. We demonstrate our approach on a genomics use-case, where the description of biological entities (e.g., genes, diseases, and drugs) is scattered across multiple datasets. In our preliminary investigation, we evaluate the QPPDs approach with real-world path queries-on biological data that are very heterogeneous in nature-in terms of performance (overall path retrieval time) and result completeness, i.e., the number of paths retrieved.

Abstract Background R has become the de-facto reference analysis environment in Bioinformatics. Plenty of tools are available as packages that extend the R functionality, and many of them target the analysis of biological networks. Several algorithms for graphs, which are the most adopted mathematical representation of networks, are well-known examples of applications that require high-performance computing, and for which classic sequential implementations are becoming inappropriate. In this context, parallel approaches targeting GPU architectures are becoming pervasive to deal with the execution time constraints. Although R packages for parallel execution on GPUs are already available, none of them provides graph algorithms. Results This work presents cuRnet, a R package that provides a parallel implementation for GPUs of the breath-first search (BFS), the single-source shortest paths (SSSP), and the strongly connected components (SCC) algorithms. The package allows offloading computing intensive applications to GPU devices for massively parallel computation and to speed up the runtime up to one order of magnitude with respect to the standard sequential computations on CPU. We have tested cuRnet on a benchmark of large protein interaction networks and for the interpretation of high-throughput omics data thought network analysis. Conclusions cuRnet is a R package to speed up graph traversal and analysis through parallel computation on GPUs. We show the efficiency of cuRnet applied both to biological network analysis, which requires basic graph algorithms, and to complex existing procedures built upon such algorithms.

In model-based product development, engineering models representing specific parts a product from diverse aspects and resolutions are used to reduce cost and lead time while systematically reducing design alternatives. Designers in the product development processes will benefit from these models if they are timely available. In order to utilize these models collected from past development processes, we propose a product development process simulation method, which computationally supports planning of the current product development process. In the proposed method, these models are used to identify the types of design tasks and dynamically instantiate them so that they can satisfy given product specifications. The method is integrated with estimation of the degree of progress in product development by using a set-based approach. The simulation procedure performs iterative updates of the degree of progress with a traversal search on the graph, which is made by extracting the attributes and their relations defined in these models, and connecting these models at the attributes in common. The use of the proposed method is illustrated through its application to the development of a wind turbine.