8th International Conference on Computer Science and Information Technology (CCSIT 2018)

November 24~25, 2018, Dubai, UAE

Accepted Papers


PROVER: AN SMT-BASED FRAMWORK FOR PROCESS VERIFICATION
Souheib Baarir, Reda Bendraou, Hakan Metin
Laboratoire d'Informatique de Paris 6, Paris, France

ABSTRACT

Business processes are used to represent the company's business and services it delivers. They are also means to create an added value to the company as well as to its customers. It is then more than critical to seriously consider the design of such processes and to make sure that they are free of any kind of inconsistencies. This paper introduces our new framework called ProVer. Three of its design decisions will be motivated: (1) the use of UML Activity Diagrams (AD) as a process modeling language, (2) the formalization of the UML AD concepts for process verification as well as a well-identified set of properties in first-order logic (FOL) and (3) the use of SMT (Satisfiability Modulo Theories) as means to verify properties spanning different process's perspectives in an optimal way. The originality of ProVer is the ability for non-experts to express properties to be verified on processes that span the control, data, time, and resource perspectives using the same tool.

ASK LESS-SCALE MARKET RESEARCH WITHOUT ANNOYING YOUR CUSTOMERS
Venkatesh Umaashankar1* and Girish Shanmugam S2
1Ericsson Research, Chennai, India.2Machine Learning Consultant, E3, Jains Green Acres, Chennai, India

ABSTRACT

Market research is generally performed by surveying a representative sample of customers with questions that includes contexts such as psycho-graphics, demographics, attitude and product preferences. Survey responses are used to segment the customers into various groups that are useful for targeted marketing and communication. Reducing the number of questions asked to the customer has utility for businesses to scale the market research to a large number of customers. In this work, we model this task using Bayesian networks. We demonstrate the effectiveness of our approach using an example market segmentation of broadband customers.

AN APPROACH FOR WEB APPLICATIONS TEST DATA GENERATION BASED ON ANALYZING CLIENT SIDE USER INPUT FIELDS
Samer Hanna1 and Hayat Jaber2
1Department of Software Engineering, Faculty of Information Technology, Philadelphia University, Jordan. 2Department of Computer Science, Faculty of Information Technology, Philadelphia University, Jordan

ABSTRACT

It is time consuming to manually generate test data for Web applications; therefore, automating this task is an important task for both practitioners and researchers in this domain. To achieve this goal, the research in this paper depends on an ontology that categorizes Web applications inputs according to input types such as number, text, and date. This research presents rules for Test Data Generation for Web Applications (TDGWA) based on the input categories specified by the ontology. Following this paper’s approach, Web applications testers will need less time and effort to accomplish the task of TDGWA. The approach had successfully been used to generate test data for different experimental and real life Web applications.

SEGMENTING RETAIL CUSTOMERS WITH ENHANCED RFM DATA USING AHYBRID REGRESSION/CLUSTERING METHOD
Fahed Yoseph, Professor Markku Heikkilä, Mohammed Malaily
Åbo Akademi University, Finalnd, Turku

ABSTRACT

Targeted marketing strategies attract interest from both industry and academia. A viable approach for gaining insight into the heterogeneity of customer purchase lifecycle is market segmentation. Conventional market segmentation models often ignore the evolution of customers’ behavior over time. Therefore, retailers often end up spendingtheir limited resources attempting to serve unprofitable customers. This study looks into the integration of Recency, Frequency, Monetary scoresand Customer Lifetime Value model, and applies the resulting data to segment customers of a medium-sized clothing and fashion accessory retailer in Kuwait. A modified regression algorithm is implemented for finding the slope for customer purchase curve. Then K-means and Expectation Maximization clustering algorithms are used to findthe sign of the curve. The purpose is to gain knowledgefrom point-of-sales data and help the retailer to make informed decisions. Cluster quality assessment concludes that the EM algorithm outperformed k-means algorithm in finding relevant segments. Finally, appropriate marketing strategies are suggested in accordance with the results generated by EM clustering algorithm

BIRD SWARM ALGORITHM FOR SOLVING THE LONG-TERM CAR POOLING PROBLEM
Zakaria BENDAOUD1 and Sidahmed BENNACEF2
1GeCode Laboratory, Department of Computer Science, Dr. Moulay Tahar University of Saida Algeria. 2Department of Computer Science, Moulay Tahar University of Saida Algeria

ABSTRACT

Carpooling consists in sharing personal vehicles to make a joint trip, in order to share the costs of fuel, toll (soon in Algeria) or simply to exchange. The purpose of this work is to benefit from web 2.0 tools in order to adopt the ideal strategy for carpooling we treated the family of long-term carpool, the problem is to find the best groups between a set of individuals who make the same trip every day and in a regular way. in order to reach our goal, we adapted a bio-inspired meta-heuristics, this technique allowed us to have very satisfying results.

VISUALISATION OF MULTI-SERVICE SYSTEM NETWORK WITH D3.JS & KDB+/Q USING WEBSOCKET
Ali Kapadiya
Kdb+ Tick-data and Analytics developer, London, UK

ABSTRACT

Visualisation of complex web of services running in a multi-service system using D3.js as frontend, KDB+/q as backend and WebSocket & JSON for communication