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Amicalola Workshop on DB-IS Research for Semantic Web and Enterprises

作者： Anyanwu, K Sheth, A Univ Georgia Dept Comp Sci Large Scale Distributed Informat Syst Lab Athens GA 30602 USA

In this paper, we introduce an approach that supports querying for Semantic Associations on the Semantic Web. Semantic Associations capture complex relationships between entities involving sequences of predicates, and sets of predicate sequences that interact in complex ways. Detecting such associations is at the heart of many research and analytical activities drat are crucial to applications in national security and business intelligence. This in combination with. the improving ability to identify entities in documents as part of automatic semantic annotation, gives a very powerful capability for antic analysis of large amounts of heterogeneous content. The approach for supporting Semantic Associations discussed in this paper has four main facets. First, it generalizes these associations into three main classes based on their structural properties, allowing us to reason about them in a. domain-independent manner. The second is the provision of an operator p for expressing queries about such associations. Third, it uses a graph data model for knowledge representation, allowing the semantic associations search techniques to be built upon the graph algorithms for paths, while integrating knowledge from the schema into the search process. The fourth facet is the use of a notion of context, which allows for restricting the search space and for context-driven ranking of results. Just as a Web search engine boles for relevant documents in the current Web, p can be seen as discovering and ranking complex relationships in the Semantic Web. In this paper, we demonstrate the need for supporting such complex semantic relationships. We also give a formal basis to the notion of Semantic Associations and give a brief discussion on our overall approach for discovering and ranking them.

关键词： semantic associations semantic relationships semantic querying complex relationship discover RDF Semantic Web graph data model

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A best-effort approach to an infrastructure for Chinese Web related research

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Frontiers of Electrical and Electronic Engineering in China 2011年第2期6卷 388-396页

作者： Weining QIAN Aoying ZHOU Minqi ZHOU Institute of Massive Computing Software Engineering InstituteEast China Normal UniversityShanghai 200241China

The design of the infrastructure for Chinese Web(CWI),a prototype system aimed at forum data analysis,is *** takes a best effort approach.1)It tries its best to extract or annotate semantics over the web data.2)It provides flexible schemes for users to transform the web data into eXtensible Markup Language(XML)forms with more semantic annotations that are more friendly for further analytical tasks.3)A distributed graph repository,called DISGR is used as backend for management of web *** paper introduces the design issues,reports the progress of the implementation,and discusses the research issues that are under study.

关键词： Chinese Web infrastructure semantic entity graph data model distributed storage

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graph Computation and Its Applications in Smart Grid 6

Graph Computation and Its Applications in Smart Grid

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IEEE 6th International Congress on Big data (Bigdata Congress)

作者： Liu, Guangyi Liu, Kewen Shi, Di Zhu, Wendong Wang, Zhiwei Chen, Xi GEIRI North Amer Inc Santa Clara CA 95054 USA GEIRI Beijing 100192 Peoples R China

ISBN: (纸本)9781538619964

Smart grid is an important platform for the exploitation of renewable energy resources and maintaining/improving the reliability of power grid. Challenges have been identified during the construction of smart grid, which calls for novel simulation, computation and data management technologies. For example, as the size of power systems continue to increase, new computational tools are needed to co-simulate the transmission and distribution networks;new data processing and analytical technologies are required to handle big data in power systems;new database technologies are required to manage huge amounts of equipment, etc. To handle these challenges, a graph computation based framework is proposed. Initial investigation of this framework shows that graph computation is a promising technology for solving many of the challenges that arise during the development of smart grid.

关键词： graph data model graph computation Big data analytics Power flow algorithm Smart grid

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graph Semantic Based Semi Structured data Management for Insurance Industry: A Case Study

Graph Semantic Based Semi Structured Data Management for Ins...

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National Conference on Computing and Communication Systems (NCCCS)

作者： Ganguly, Rita Sarkar, Anirban BC Roy Engn Coll Dept Comp Applicat Durgapur India

ISBN: (纸本)9781467319522;9781467319515

This paper has proposed a case study of insurance industry through implementing the graph - semantic based conceptual model for semi-structured database system, called GOOSSDM. The GOOSSDM reveals a set of concepts to the conceptual level design phase of semi-structured database system, which are understandable to the users, independent of implementation issues and provide a set of graphical constructs to facilitate the designers of such system. A rule based transformation mechanism of GOOSSDM schema into the equivalent XML Schema Definition (XSD) also has been applied in this paper.

关键词： XML XSD Conceptual modeling graph data model Semi-structured data

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Research of data graph Mining based on Telecommunication Customers

Research of Data Graph Mining based on Telecommunication Cus...

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3rd International Conference on Computer-Aided Design, Manufacturing, modeling and Simulation(CDMMS 2013)

作者： Gao, Shang Li, Meimei Tsinghua Univ Beijing 100084 Peoples R China China United Network Commun Ltd Beijing Peoples R China Beijing Jiaotong Univ Beijing Peoples R China

ISBN: (纸本)9783037859100

With the rapid development of the number of mobile phone users has accumulated a large number of graph data, graph data mining has gradually become a hot area of research. Traditional data such as clustering, classification, frequent pattern mining gradually extended to the field of graph data mining research. Introduced at this stage graph data mining technology research progress, summarizes the characteristics of the graphical data mining, practical significance, the main problem, and scenarios to discuss and forecast chart data, especially research on uncertain graph data become trends and hot spots.

关键词： Telecommunication customers data mining graph data model clustering classification

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Implementing Real Time Recommendation Systems using graph Algorithms & Exploring graph Analytics in a graph database Platform (Neo4j)

Implementing Real Time Recommendation Systems using Graph Al...

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作者： Amit Kumar Dublin Business School

学位级别：硕士

Recommendation Systems play a very important role in our lives in the modern era. With the advent of Big data in recent years, an enormous amount of information (in both structured and un structured data formats) is being generated every second from various data sources. Recommendation Systems are very helpful for generating meaningful insights from massive amounts of data. Slower batch approaches are enhanced by real time recommendations enabled by storing data in a graph database platform. This dissertation aims to build and implement a real time recommendation system using different graph algorithms in Neo4j, the current leader in graph operational database management systems. The requirements or use cases for this research were proposed by the AI and Analytics Team of Neo4j, for their ongoing research and development activities. Various research papers were studied to get an overview of various graph algorithms currently used in recommendation systems in graph databases. A customized graph data model was implemented to provide solutions for the research questions. Cypher, the Neo4j query language was used to implement a selection of recommendation graph algorithms on this data model. The graph algorithms used were Overlap Similarity, Cosine Similarity and PageRank. For providing a comparison between traditional and graph databases, FP-Growth, a traditional Association Rule Algorithm, was implemented using Rapid Miner, a leading data Mining Tool, showcasing a particular use case using a traditional approach. A Python Script was developed to prepare the data for loading to the customized graph data model. Also, a data profiling or statistical analysis was performed on the loaded data providing a thorough analysis of the structure, contents and meta data of the loaded data. graph analytics were only been introduced for an operational graph database management system in the fourth quarter of 2017. The results obtained from this research highlight the enormous potent

关键词： graph databases graph algorithms overlap similarity cosine similarity pagerank neo4j fp-growth recommendation systems python cypher rapid miner association rules graph data model real time

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Design of a data model for social network applications

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JOURNAL OF dataBASE MANAGEMENT 2007年第4期18卷 51-79页

作者： Mitra, Susanta Bagchi, Aditya Bandyopadhyay, A. K. IIIT Kolkata India Indian Stat Inst Comp & Stat Serv Ctr Kolkata India Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 W Bengal India

A social network defines the structure of a social community like an organization or institution, covering its members and their inter-relationships. Social relationships among the members of a community can be of different types like friendship, kinship, professional, academic, and so forth. Traditionally, a social network is represented by a directed graph. Analysis of graph structure representing a social network is done by the sociologists to study a community. Hardly any effort has been made to design a data model to store and retrieve social-network-related data. In this paper, an object-relational graph data model has been proposed for modeling a social network. The objective is to illustrate the power of this generic model to represent the common structural and node-based properties of different social network applications. A novel, multi-paradigm architecture has been proposed to efficiently manage the system. New structural operators have been defined in the paper and the application of these operators has been illustrated through query examples. The completeness and the minimality of the operators have also been shown.

关键词： graph data model multi-paradigm architecture object-relational data model social network

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data continuity and traceability in complex manufacturing systems: a graph-based modeling approach

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2021年第5期34卷 549-566页

作者： Kuhn, Marlene Franke, Joerg Friedrich Alexander Univ Erlangen Nuremberg Inst Factory Automat & Prod Syst Nurnberg Germany

Modern industrial production systems are facing the challenge of interconnecting and exploiting the increasing amount of data from smart products, software systems, and IIoT devices. One of the main tasks in this context is the implementation of a data-continuous traceability system, which interconnects the heterogeneous data objects over the entire production flow. While traceability maturity is already high in regulated and process-driven industries, the established traceability solutions cannot be successfully applied to customized and volatile industries. In this research, a graph-based traceability modeling methodology is presented that allows the systematic development of data continuity and integration in manufacturing. Based on the traceability methodology, the graph data model and architecture are specified, which are aligned to the requirements of customized and complex manufacturing systems. The model is implemented and validated based on a Neo4j graph database for the use case of the manufacturing process of automotive electrical systems. This research overcomes the shortcomings of state-of-the-art traceability models by shifting the focus to the relationships between traceability-relevant data objects. The proposed graph-based traceability model is able to capture multi-hierarchical product structures and is less dependent on physical object identification, making it more applicable to customized and complex manufacturing.

关键词： Traceability mes manufacturing information systems data continuity graph data model neo4j

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A system architecture for parallel analysis of flux-balanced metabolic pathways

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2020年 88卷 107309页

作者： Arabzadeh, Mona Sedighi, Mehdi Zamani, Morteza Saheb Marashi, Sayed-Amir Amirkabir Univ Technol Dept Comp Engn & Informat Technol Tehran Iran Univ Tehran Coll Sci Dept Biotechnol Tehran Iran

Elementary flux mode (EFM) analysis is a well-studied method in constraint-based modeling of metabolic net-works. In EFM analysis, a network is decomposed into minimal functional pathways based on the assumption of balanced metabolic fluxes. In this paper, a system architecture is proposed that approximately models the functionality of metabolic networks. The AND/OR graph model is used to represent the metabolic network and each processing element in the system emulates the functionality of a metabolite. The system is implemented on a graphics processing unit (GPU) as the hardware platform using CUDA environment. The proposed architecture takes advantage of the inherent parallelism in the network structure in terms of both pathway and metabolite traversal. The function of each element is defined such that it can find flux-balanced pathways. Pathways in both small and large metabolic networks are applied to the proposed architecture and the results are discussed.

关键词： Elementary flux mode (EFM) graph data model graphics processing unit (GPU) Metabolic pathways

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IFC-Based Framework to Move beyond Individual Building Elements toward Configuring a Higher Level of Prefabrication

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2013年第3期27卷 243-253页

作者： Khalili, Alireza Chua, David K. H. Natl Univ Singapore Dept Civil & Environm Engn Singapore 117576 Singapore Natl Univ Singapore Dept Civil & Environm Engn Singapore 119260 Singapore

Prefabrication is a form of industrialization that transfers some stages of the construction project from fields to an off-site production facility. The most widely used form of prefabrication in construction is the use of prefabricated individual building elements in which a particular part or form is repeated many times. However, the benefits to the construction project may be increased by moving beyond prefabrication of individual building elements toward configuring a higher level of prefabrication. In this paper, an industry foundation class (IFC)-based system is developed to configure groupings of precast elements for a prefabricated building to minimize the total number of components so as to reduce the production, transportation, and installation cost. The framework uses three-dimensional computer-aided design (3D CAD) software that is compatible with IFC. Geometrical and topological relationships of elements are extracted from IFC and modeled as a graph data model (GDM). The system performs analysis on the graph model based on the attributes of the building elements to generate all possible configurations for precasting. Constructability rules are applied to reduce the solutions to feasible configurations. The functionality of the developed framework is verified by considering an illustrative example of a residential building. Results showed that applying the new configurations of precast components can contribute up to 15% reduction in the total cost compared with the traditional elemental approach. DOI: 10.1061/(ASCE)CP.1943-5487.0000203. (C) 2013 American Society of Civil Engineers.

关键词： Prefabrication graph data model Industry foundation class (IFC) Three-dimensional computer-aided design (3D CAD)

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