2020 IEEE International Conference on Services Computing (SCC)最新文献_第2页

Data Provenance for Complex Event Processing Invoking Composition of Services 调用服务组合的复杂事件处理的数据来源

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00027

Malik Khalfallah, P. Ghodous

引用次数: 0

App Competition Matters: How to Identify Your Competitor Apps? 应用竞争问题:如何识别竞争对手应用?

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00055

Md. Kafil Uddin, Qiang He, Jun Han, C. Chua

{"title":"App Competition Matters: How to Identify Your Competitor Apps?","authors":"Md. Kafil Uddin, Qiang He, Jun Han, C. Chua","doi":"10.1109/SCC49832.2020.00055","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00055","url":null,"abstract":"App stores, such as Google Play and Apple Store, contain abundance of information, including descriptions and reviews of various apps. They provide valuable information for app developers to learn about their own apps as well as similar apps for improving their apps, e.g., adding popular features or removing unpopular features. A place to start this learning process is to identify the competitor apps of a given target app in terms of their features, popularity as well as other relevant aspects. Given the large number of apps and the large amount of information available about these apps, it is a very challenging task for app developers to effectively and efficiently identify competitor apps. In this paper, we introduce a novel approach for identifying the competitor apps of a given target app, which includes three major components. Firstly, we identify the factors that characterise the competition between apps. Then, based on the identified competition factors, we extract and process the relevant information from the app store about the target app and the apps in the same app category. Finally, we cluster the apps based on their similarity across all the competition factors and identify those apps that are in the same cluster as the target app as its competitor apps. We evaluate our approach by comparing its results with corresponding search results in (1) Google Trends and (2) Google Search. The results show that our approach is effective in identifying competitor apps.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128142236","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

Analytics on Health of Mobile Software Ecosystem Based on the Internal Operating Mechanism 基于内部运行机制的移动软件生态系统健康度分析

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00042

Jianmao Xiao, Shizhan Chen, Shiping Chen, Chao Gao, Hongyue Wu, Xiao Xue, Zhiyong Feng

{"title":"Analytics on Health of Mobile Software Ecosystem Based on the Internal Operating Mechanism","authors":"Jianmao Xiao, Shizhan Chen, Shiping Chen, Chao Gao, Hongyue Wu, Xiao Xue, Zhiyong Feng","doi":"10.1109/SCC49832.2020.00042","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00042","url":null,"abstract":"In addition to publishing and downloading mobile apps, Mobile App Store (MAS) has become the most important ecosystem on mobile smart devices, i.e., Mobile Software Ecosystem (MSECO). However, most of the existing work focus on the analysis of a single entity in MSECO, and rarely analyze the interaction between the entities (such as users, developers, etc.) in MSECO as well as the comprehensive effect of each entity to the entire ecosystem health. In this paper, we propose a method based on computational experiments to simulate and analyze the complex and dynamic interaction of various entities and, how these entities impact on health of MSECO. Firstly, a requirement-driven MSECO model named R-MSECO is established to break down the entire MSECO, which includes user-app interaction, developers-requirements interaction and macro-control of mobile platform three sub-models. Secondly, the health measurement method is proposed to measure the health of MSECO. Finally, we simulate the impact of the dynamic interaction of various entities on the health of MSECO based on computational experiments. The experimental results show that our method is helpful for mobile users to better understanding of the current state of MSECO as well as can provide a reference for developers and platform managers to make development and operation decisions, which is of great significance for the continued healthy development of MSECO.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132050067","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: 1

The Research of Link Prediction in Knowledge Graph based on Distance Constraint 基于距离约束的知识图链接预测研究

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00018

Li Wei, Fangfang Liu

{"title":"The Research of Link Prediction in Knowledge Graph based on Distance Constraint","authors":"Li Wei, Fangfang Liu","doi":"10.1109/SCC49832.2020.00018","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00018","url":null,"abstract":"Large-scale knowledge graphs have a lot of hidden knowledge which has not been discovered, so the link prediction of the knowledge graph is an important topic. Translation models represented by TransE are the well-researched algorithms of link prediction. They project the entities and the relations in the knowledge graphs into some continuous vector spaces, and adjust the vector representations of the relations and the entities according to each piece of knowledge. However, in the case of a non-1-to-1 relationship, multiple entity vectors will compete for the same coordinate position in the space. Aiming at this problem, this paper proposes an improved method. By imposing a distance constraint on the competitive entities of a non-1-to-1 relationship, we can narrow the differences between them. Each entity will consider the other competitive entities while adapting itself to fit a triplet, so as to reach the status that each competitive entity is close to the coordinate point of the competition as a whole. Distance constraint can be applied to the existing translation models as a means of optimization. Experiments are conducted on the datasets: FB15K and WN18, and the experimental results show that the method we proposed is effective.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127914920","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: 1

desc2tag: A Reinforcement Learning Approach to Mashup Tag Recommendation desc2tag: Mashup标签推荐的强化学习方法

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00073

R. Anarfi, Benjamin A. Kwapong, K. K. Fletcher

引用次数: 0

Multi-factor-based Motion Detection for Server Rack Doors Left Open 基于多因素的服务器机架门敞开运动检测

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00067

Ruriko Kudo, Yasuharu Katsuno, Fumiko Satoh

引用次数: 2

A Reference Method for Performance Evaluation in Big Data Architectures 大数据架构中性能评估的参考方法

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00044

Wictor Souza Martins, B. Kuehne, Rafael Ferreira Sobrinho, F. Preti

{"title":"A Reference Method for Performance Evaluation in Big Data Architectures","authors":"Wictor Souza Martins, B. Kuehne, Rafael Ferreira Sobrinho, F. Preti","doi":"10.1109/SCC49832.2020.00044","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00044","url":null,"abstract":"This paper presents a reference method for performance evaluation in Big Data architectures, called by Improvement Method for Big Data Architectures (IMBDA) aiming to increase the performance, and consequently raising the quality of service provided. The method will contribute to small businesses and startups that have limited financial re-sources (impossible to invest in market solutions). The proposed approach considers the relationship of the processes in a data processing flow to find possible bottlenecks and optimization points. To this end, IMBDA collects system logs to compose functional metrics (e.g., processing time) and non-functional metrics (e.g., CPU and memory utilization, and other cloud computing infrastructure resources). The system stores these metrics in an external data analysis tool that investigates the correlation of performance between processes. The reference method applies to the architecture of a Big Data application, which provides solutions in fleet logistics. With the use of IMBDA, it was possible to identify performance bottlenecks, allowing the reconfiguration of the architecture to increase service quality at the lowest possible cost.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117026327","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: 0

Bringing Semantics to Support Ocean FAIR Data Services with Ontologies 引入语义支持海洋公平数据服务与本体

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00011

Xiaoli Ren, Xiaoyong Li, Kefeng Deng, Kaijun Ren, Aolong Zhou, Junqiang Song

{"title":"Bringing Semantics to Support Ocean FAIR Data Services with Ontologies","authors":"Xiaoli Ren, Xiaoyong Li, Kefeng Deng, Kaijun Ren, Aolong Zhou, Junqiang Song","doi":"10.1109/SCC49832.2020.00011","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00011","url":null,"abstract":"With the increasing attention to ocean and the development of data-intensive sciences, a large amount of ocean data has been acquired by various observing platforms and sensors, which poses new challenges to data management and utilization. Typically, nowadays we target to move ocean data management toward the FAIR principles of being findable, accessible, interoperable, and reusable. However, the data produced and managed by different organizations with wide diversity, various structures and increasing volume make it hard to be FAIR, and one of the most critical reason is the lack of unified data representation and publication methods. In this paper, we propose novel techniques to try to solve the problem by introducing semantics with ontologies. Specifically, we first propose a unified semantic model named OEDO to represent ocean data by defining the concepts of ocean observing field, specifying the relations between the concepts, and describing the properties with ocean metadata. Then, we further optimize the state-of-the-art quick service query list (QSQL) data structure, by extending the domain concepts with WordNet to improve data discovery. Moreover, based on the OEDO model and the optimized QSQL, we propose an ocean data service publishing method called DOLP to improve data discovery and data access. Finally, we conduct extensive experiments to demonstrate the effectiveness and efficiency of our proposals.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127161240","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: 1

Suitability-based Task Assignment in Crowdsourcing Markets 众包市场中基于适用性的任务分配

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00054

Pengwei Wang, Zhen Chen, Zhaohui Zhang

{"title":"Suitability-based Task Assignment in Crowdsourcing Markets","authors":"Pengwei Wang, Zhen Chen, Zhaohui Zhang","doi":"10.1109/SCC49832.2020.00054","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00054","url":null,"abstract":"Crowdsourcing web services has received much attention in recent years, which has been widely used in many fields, and provides important human computing services for the rapid development of AI. Crowdsourcing knowledge acquisition is one of the most important applications, which includes a series of work such as image annotation and picture classification. However, due to the differences in the difficulty of these tasks and the uncertainty of workers, how to make a reasonable task assignment while ensuring the completion of tasks becomes a big challenge. To this end, we introduce WordNet external knowledge base to help determine the difficulty of picture classification tasks. We refer to the e-sports rank mechanism and use dynamic update strategy to assess the actual ability of workers. A novel criterion affinity based on the weighted Euclidean distance with penalty factor is proposed to measure the suitability between tasks and workers. On this basis, the Kuhn-Munkres (KM) algorithm is used to solve the weighted bipartite graph matching problem. Through comparative experiments, the effectiveness of our proposed method is verified.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124447303","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: 3

A Novel Data-to-Text Generation Model with Transformer Planning and a Wasserstein Auto-Encoder 一种具有变压器规划和Wasserstein自编码器的新型数据到文本生成模型

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00051

Xiaohong Xu, T. He, Huazhen Wang

{"title":"A Novel Data-to-Text Generation Model with Transformer Planning and a Wasserstein Auto-Encoder","authors":"Xiaohong Xu, T. He, Huazhen Wang","doi":"10.1109/SCC49832.2020.00051","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00051","url":null,"abstract":"Existing methods for data-to-text generation have difficulty producing diverse texts with low duplication rates. In this paper, we propose a novel data-to-text generation model with Transformer planning and a Wasserstein auto-encoder, which can convert constructed data to coherent and diverse text. This model possesses the following features: Transformer is first used to generate the data planning sequence of the target text content (each sequence is a subset of the input items that can be covered by a sentence), and then the Wasserstein Auto-Encoder(WAE) and a deep neural network are employed to establish the global latent variable space of the model. Second, text generation is performed through a hierarchical structure that takes the data planning sequence, global latent variables, and context of the generated sentences as conditions. Furthermore, to achieve diversity of text expression, a decoder is developed that combines the neural network with the WAE. The experimental results show that this model can achieve higher evaluation scores than the existing baseline models in terms of the diversity metrics of text generation and the duplication rate.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"333 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122140862","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: 2