SEKE2016

Proceedings of the Twenty-Eighth International Conference on Software Engineering & Knowledge Engineering

San Francisco Bay July 1-3

SEKE

Proceedings of the Twenty-Eighth International Conference on Software Engineering & Knowledge Engineering

San Francisco Bay July 1-3 2016

Copyright © 2016 Printed byKSI Research Inc. & Knowledge Systems Institute Graduate School156 Park SquarePittsburgh, PA 15238 USATel: +1-412-606-5022Fax: +1-847-679-3166Email: seke@ksiresearch.orgWeb: http://ksiresearchorg.ipage.com/seke/seke16.htmlPrinted in USA, 2016ISBN 1-891706-39-X (paper)ISSN 2325-9000 (print)

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SEKE2016 Table of Contents

Evaluating the Representation of User Interface Elements in Feature Models: anEmpirical Study (S) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 628Ildevana Poltronieri Rodrigues, Ana Paula Bacelo, Milene Selbach Silveira, Marcia deBorba Campos and Elder de Macedo Rodrigues

Towards a Systematic Approach to Graph Data Modeling: Scenario-based Design andExperiences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634Mengjia Zhao, Yan Liu and Peng Zhou

Software Engineering Methodology

An Agile Methodology for Reengineering Object-Oriented Software . . . . . . . . . . . . . . . . . . . . . . . 638Anam Sahoo, David Kung and Sanika Gupta

A Content-Based Approach for Recommending UML Sequence Diagrams (S) . . . . . . . . . . . . . 644Thaciana G. O. Cerqueira, Franklin Ramalho and Leandro Balby Marinho

Software Clustering using Hybrid Multi-Objective Black Hole Algorithm (S) . . . . . . . . . . . . . . 650Kawal Jeet and Renu Dhir

Poster

MABT - a multiagent-based toolkit for transforming existing systems into self-adaptivesystems (P) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654Lu Wang, Qingshan Li, Yishuai Lin and Hua Chu

An E�cient Algorithm to Identify Minimal Failure-Causing Schemas from ExhaustiveTest Suite (P). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655Yuanchao Qi, Qi Wang, Chiya Xu, Tieke He and Ziyuan Wang

A qualitative analysis of the adhrence between the Information Tecnology SolutionAquisition Guide, for Brazilian Federal Public Administration and, the CMMI models (P) 657Luiz Sergio Placido Da Silva, Renata Moreira, Alexandre Marcos Lins de Vasconcelos,Mauricio Ronny Souza and Suzana Candido de Barros Sampaio

Early Detection of Suicide Using Big-Data Analytics in Real Time (P) . . . . . . . . . . . . . . . . . . . 659Hardik Patel and Cheng-Yuan Hsieh

Authors’ Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

Progam Committee Reviewers’ Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .A-10

External Reviewers’ Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-14

Note:(S) indicates a short paper.(P) indicates a poster.

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Towards a Systematic Approach to Graph DataModeling: Scenario-based Design and Experiences

Mengjia ZHAO⇤, Yan LIU†, Peng ZHOU‡

School of Software Engineering, Tongji UniversityShanghai, China

Email: ⇤1434319@tongji.edu.cn, †yanliu.sse@tongji.edu.cn, ‡1435855@tongji.edu.cn

Abstract—Graph database is recently being adopted by dataanalytic systems as an appealing alternative to relational databasefor the management of large-scale inherent graph-like data.A great challenge of leveraging graph database technologiesis to model a problem domain into graph. However, in theabsence of considering application requirements or goals, currentgraph data modeling approaches seem to be invalid. This paperpresents an exploration of a systematic approach for graph datamodeling—SuMo. Starting from real world scenarios, require-ments are transformed into a domain model, which acts asan intermediate model in SuMo, captures modeling features ofthat domain. SuMo defines a set of rules for the subsequenttransformation of this domain model to produce a graph model.We applied SuMo to the modeling of a data-intensive analyticsystem using real datasets as an illustrating example to clarifyour main idea. SuMo is empirically evaluated in terms ofquery performance, the experimental results indicate promisingfeasibility and efficiency. The major contribution of our work isa preliminary graph data modeling approach based on scenarios.

Keywords-graph data modeling; data analytic system; scenario-

based modeling; model-driven design

I. INTRODUCTION

Graph database has recently gained popularity rapidly be-cause of a need to effectively manage large-scale inherentgraph-like data [1]. Social network, biology, semantic web andhealth-care are typical application domains that contain suchkind of data [2][3][4]. It has been observed that graph databaseis usually preferable to relational database in managing dataof these domains since the latter hardly captures the inherentgraph structure [5]. Motivated by scalability and performanceneeds of current applications, graph database is increasinglyadopted by data analytic systems.

The analysis of relationships becomes important when deal-ing with highly connected data. A graph consists of a finiteset of nodes, and a finite set of edges defining relationshipsbetween these nodes. In graph database, data is stored as graphand is accessed by queries as graph traversal operations. Com-pared with the stores of relational database, relationships aretreated as first-class citizens in graph database [6], expressedin a more straightforward way. Moreover, queries involvingcomplex and inefficient join operations are transformed intograph traversals. The execution time of graph traversal isproportional only to the size of traversed part [6].

DOI reference number: 10.18293/SEKE2016-119

Graph data modeling is the process in which an arbitraryapplication domain described as a connected graph of nodesand edges, it is not a context-free process, but a purposiveabstraction related with application requirements. As it hap-pens with relational database, the design of relational modelcan start from ER model, such kind of “springboard” is alsoconducive to the design of graph model.

Guidelines and principles on graph data modeling can befound from various sources, including books, technical reports,graph database online community and practitioners’ blogs.There has also been an involvement of academic papers in thestudy of graph data modeling, many works focus on convertingexisting data from relational to graph model automatically; anumber of researchers aim at generic graph data modeling ap-proaches. However, most of these works, simply demonstratedsome rudimentary strategies of graph data modeling, ratherthan connecting to practical application requirements. As aresult, practitioners can only find some scattered modelingguidelines.

Currently, graph data modeling is still based on best prac-tices and probably unproved guidelines, which are usuallyrelevant to specific systems [5]. This paper aims to proposea systematic graph data modeling approach at a “proof ofconcept” stage. The primary challenge that come is, how toanalyze requirements of application domain to support graphdata modeling? Translating these requirements to produce amodel brings more challenges.

In this paper, we are exploring a systematic graph datamodeling approach, using scenarios to start out the modelingprocess. We suggest an adapted domain modeling approachwith strategies to abstract key concepts from scenarios. A setof rules are defined for the transformation of domain modelto graph model. Our aim is to match application requirementsand facilitate the design of graph-based analytic systems. Wealso provide experiments, in terms of query performance,showing the advantages of our approach with respect to a naıveapproach. The main contribution of this paper is a sketch ofa systematic graph data modeling approach.

The rest of this paper is organized as follows, Section IIpresents related work. The proposed approach is presented inSection III. A case study is demonstrated in Section IV. Wedesign and conduct a group of experiments to evaluate ourapproach, as well as discuss the results in Section V. Theconclusion and future works are in Section VI.

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