2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA)最新文献_第3页

A technique to evaluate software evolution based on architecture metric 一种基于体系结构度量来评估软件演化的技术

2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2016-06-08 DOI: 10.1109/SERA.2016.7516156

Bixin Li, Li Liao, Jingwen Si

引用次数: 1

A survey of Western Australian Software Businesses an exlporative web content analysis 西澳大利亚软件企业的调查一个探索性的网络内容分析

2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2016-06-08 DOI: 10.1109/SERA.2016.7516150

Arunasalam Sambhanthan, V. Potdar

引用次数: 1

Stakeholder's expected value of Enterprise Architecture: An Enterprise Architecture solution based on stakeholder perspective 涉众对企业架构的期望值:基于涉众视角的企业架构解决方案

2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2016-06-08 DOI: 10.1109/SERA.2016.7516152

Ira Puspitasari

{"title":"Stakeholder's expected value of Enterprise Architecture: An Enterprise Architecture solution based on stakeholder perspective","authors":"Ira Puspitasari","doi":"10.1109/SERA.2016.7516152","DOIUrl":"https://doi.org/10.1109/SERA.2016.7516152","url":null,"abstract":"Many enterprises develop and adopt Enterprise Architecture (EA) to achieve IT and business alignment and to support strategic business excellence. However, EA contribution to enterprise goals is questionable. The EA implementation is ineffective because stakeholders are reluctant to actively adopt it in their enterprise life. The reluctance happens because the EA product does not accommodate stakeholder's expected values. No matter how excellent an EA is, if the stakeholder rejects its content, it becomes meaningless and loses its strategic functionality. Thus, EA architect needs to identify and accommodate stakeholder's expected value. The next problem arises when a stakeholder's expected value conflicts with others' expected value or enterprise objective. This paper proposes a solution based on the stakeholder perspective to improve a successful and smooth EA implementation. The proposed approach consists of the stakeholder's expected value analysis scheme, the EA stakeholder profile catalog, and the priority matrix. The analysis scheme is used to identify EA stakeholder, to create the profile catalog, and to analyze stakeholder's expected value. The purpose of the priority matrix is to solve potentially conflicted expected values between stakeholders by prioritizing the value fulfillment based on stakeholder's contribution and concern. The preliminary validation by EA architects suggests that the proposed approach is feasible and usable to be applied in the EA development process.","PeriodicalId":412361,"journal":{"name":"2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126002232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Reliability modeling and verification of BPEL-based web services composition by probabilistic model checking 通过概率模型检查对基于bpel的web服务组合进行可靠性建模和验证

2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2016-06-08 DOI: 10.1109/SERA.2016.7516140

C. Mi, Huai-kou Miao, Jinyu Kai, Honghao Gao

引用次数: 6

Towards understanding and exploiting developers' emotional variations in software engineering 致力于理解和利用软件工程中开发人员的情感变化

2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2016-06-08 DOI: 10.1109/SERA.2016.7516145

M. R. Islam, M. Zibran

引用次数: 41

Machine learning-based mobile threat monitoring and detection 基于机器学习的移动威胁监测与检测

2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2016-06-08 DOI: 10.1109/SERA.2016.7516130

W. G. Hatcher, David Maloney, Wei Yu

{"title":"Machine learning-based mobile threat monitoring and detection","authors":"W. G. Hatcher, David Maloney, Wei Yu","doi":"10.1109/SERA.2016.7516130","DOIUrl":"https://doi.org/10.1109/SERA.2016.7516130","url":null,"abstract":"Mobile device security must keep up with the increasing demand of mobile users. Smartphones are every day becoming connected to more devices and services, interacting with the growing Internet of things. Every new service, and connection, creates a new pathway for intrusion and data theft. Each intrusion can yield further opportunities for breaches of corporate and enterprise infrastructure, and significant cost. In our study, we propose a mobile security platform that combines our developed security web server, analysis module, and Android OS application, with the Google Cloud Messaging service for queued and targeted device messaging. In the cloud, the developed LAMP (Linux, Apache, MySQL, PHP) server sends, receives, and stores data from a connected device via the corresponding Android OS application. The data consists of system information for device identification, and application data to be distributed to the analysis module for malicious content to be extracted and identified. The analysis module, utilizing the Weka software, performs both static and dynamic analyses to detect Android malware, simultaneously providing rapid and intuitive security with predictive capabilities. The server additionally provides device status visualization and manual security operations.","PeriodicalId":412361,"journal":{"name":"2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126276342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Efficient genetic K-Means clustering for health care knowledge discovery 医疗保健知识发现的高效遗传k均值聚类

2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2016-06-08 DOI: 10.1109/SERA.2016.7516127

Ahmed Alsayat, H. El-Sayed

{"title":"Efficient genetic K-Means clustering for health care knowledge discovery","authors":"Ahmed Alsayat, H. El-Sayed","doi":"10.1109/SERA.2016.7516127","DOIUrl":"https://doi.org/10.1109/SERA.2016.7516127","url":null,"abstract":"Data mining and machine learning are becoming the most interesting research areas and increasingly popular in health organizations. The hidden patterns among patients data can be extracted by applying data mining. The techniques and tools of data mining are very helpful as they provide health care professionals with significant knowledge toward a decision. Researchers have shown several utilities of data mining techniques such as clustering, classification, and regression in health care domain. Particularly, clustering algorithms which help researchers discover new insights by segmenting patients and providing them with effective treatments. This paper, reviews existing methods of clustering and present an efficient K-Means clustering algorithm which uses Self Organizing Map (SOM) method to overcome the problem of finding number of centroids in traditional K-Means. The SOM based clustering is very efficient due to its unsupervised learning and topology preserving properties. Two-staged clustering algorithm uses SOM to produce the prototypes in the first stage and then use those prototypes to create clusters in the second stage. Two health care datasets are used in the proposed experiments and a cluster accuracy metric was applied to evaluate the performance of the algorithm. Our analysis shows that the proposed method is accurate and shows better clustering performance along with valuable insights for each cluster. Our approach is unsupervised, scalable and can be applied to various domains.","PeriodicalId":412361,"journal":{"name":"2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117086409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Social media analysis using optimized K-Means clustering 使用优化K-Means聚类的社交媒体分析

2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2016-06-08 DOI: 10.1109/SERA.2016.7516129

Ahmed Alsayat, H. El-Sayed

{"title":"Social media analysis using optimized K-Means clustering","authors":"Ahmed Alsayat, H. El-Sayed","doi":"10.1109/SERA.2016.7516129","DOIUrl":"https://doi.org/10.1109/SERA.2016.7516129","url":null,"abstract":"The increasing influence of social media and enormous participation of users creates new opportunities to study human social behavior along with the capability to analyze large amount of data streams. One of the interesting problems is to distinguish between different kinds of users, for example users who are leaders and introduce new issues and discussions on social media. Furthermore, positive or negative attitudes can also be inferred from those discussions. Such problems require a formal interpretation of social media logs and unit of information that can spread from person to person through the social network. Once the social media data such as user messages are parsed and network relationships are identified, data mining techniques can be applied to group different types of communities. However, the appropriate granularity of user communities and their behavior is hardly captured by existing methods. In this paper, we present a framework for the novel task of detecting communities by clustering messages from large streams of social data. Our framework uses K-Means clustering algorithm along with Genetic algorithm and Optimized Cluster Distance (OCD) method to cluster data. The goal of our proposed framework is twofold that is to overcome the problem of general K-Means for choosing best initial centroids using Genetic algorithm, as well as to maximize the distance between clusters by pairwise clustering using OCD to get an accurate clusters. We used various cluster validation metrics to evaluate the performance of our algorithm. The analysis shows that the proposed method gives better clustering results and provides a novel use-case of grouping user communities based on their activities. Our approach is optimized and scalable for real-time clustering of social media data.","PeriodicalId":412361,"journal":{"name":"2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115946267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

An experimental exploration of the impact of multi-level packet loss on network intrusion detection 多级丢包对网络入侵检测影响的实验研究

2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2016-06-08 DOI: 10.1109/SERA.2016.7516124

Sidney C. Smith, R. Hammell, Kin W. Wong, Carlos J. Mateo

引用次数: 6

Waste management strategies for Software Development companies: An explorative text analysis of business sustainability reports 软件开发公司的废物管理策略:商业可持续性报告的探索性文本分析

2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2016-06-08 DOI: 10.1109/SERA.2016.7516144

Arunasalam Sambhanthan, V. Potdar

{"title":"Waste management strategies for Software Development companies: An explorative text analysis of business sustainability reports","authors":"Arunasalam Sambhanthan, V. Potdar","doi":"10.1109/SERA.2016.7516144","DOIUrl":"https://doi.org/10.1109/SERA.2016.7516144","url":null,"abstract":"This paper documents waste management strategies used by Indian Software Development firms. Indian software development giants' corporate sustainability reports were subjected to content analysis using Nvivo qualitative analysis tool. The content analysis reveals that there are a number of waste types such as e-waste, waste water, recycling waste, construction waste, hazardous waste, solid waste, organic waste, packaging waste, paper waste, wet waste, plastic waste, food waste, dry waste, consumer waste, biomedical waste and biodegradable waste which are recorded in the sustainability reports of Indian software development firms. The key implications includes the usage of government authorized recyclers and other government supported initiatives to support the e-waste recycling and disposal, due-diligence audit process for e-waste dismantlers and recyclers to verify their HSE legal compliances and government regulations pertaining to e-waste management and other specific guidelines to assist in waste management. The paper highlights a number of key waste management strategies and tactics used by Indian software development firms which could be used by the software development organizations around the world as best practices.","PeriodicalId":412361,"journal":{"name":"2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122935478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6