A data-driven game theoretic strategy for developers in software crowdsourcing: a case study

Crowdsourcing has the advantages of being cost-effective and saving time, which is a typical embodiment of collective wisdom and community workers’ collaborative development. However, this development paradigm of software crowdsourcing has not been used widely. A very important reason is that requesters have limited knowledge about crowd workers’ professional skills and qualities. Another reason is that the crowd workers in the competition cannot get the appropriate reward, which affects their motivation. To solve this problem, this paper proposes a method of maximizing reward based on the crowdsourcing ability of workers, they can choose tasks according to their own abilities to obtain appropriate bonuses. Our method includes two steps: Firstly, it puts forward a method to evaluate the crowd workers’ ability, then it analyzes the intensity of competition for tasks at Topcoder.com—an open community crowdsourcing platform—on the basis of the workers’ crowdsourcing ability; secondly, it follows dynamic programming ideas and builds game models under complete information in different cases, offering a strategy of reward maximization for workers by solving a mixed-strategy Nash equilibrium. This paper employs crowdsourcing data from Topcoder.com to carry out experiments. The experimental results show that the distribution of workers’ crowdsourcing ability is uneven, and to some extent it can show the activity degree of crowdsourcing tasks. Meanwhile, according to the strategy of reward maximization, a crowd worker can get the theoretically maximum reward

Applications of ontology in the Internet of Things: a systematic analysis

Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions

Data distribution and exploitation in a global microservice artefact observatory

​© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cloud computing and specifically the microservice architecture pattern is becoming an increasingly prominent paradigm in computer science. Many modern cloud applications are composed of a variety of different microservices, each potentially built in different languages, using different technologies and a different software artefact structure. What is needed is the capability to monitor this rapidly expanding field and leverage the data to enable further research and development of microservice architectures. Drawing inspiration from the global observatories used in geoscience and astronomy, the aim of this research initiative is the establishment of a global observatory for microservice artefacts, allowing the aggregation of data from different hubs and the execution of dynamic analysis on them

Applications of ontology in the internet of things: A systematic analysis

Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions

BIG DATA MINING TOOLS FOR UNSTRUCTURED DATA: A REVIEW

Big data is a buzzword that is used for a large size data which includes structured data, semi-structured data and unstructured data. The size of big data is so large, that it is nearly impossible to collect, process and store data using traditional database management system and software techniques. Therefore, big data requires different approaches and tools to analyze data. The process of collecting, storing and analyzing large amount of data to find unknown patterns is called as big data analytics. The information and patterns found by the analysis process is used by large enterprise and companies to get deeper knowledge and to make better decision in faster way to get advantage over competition. So, better techniques and tools must be developed to analyze and process big data. Big data mining is used to extract useful information from large datasets which is mostly unstructured data. Unstructured data is data that has no particular structure, it can be any form. Today, storage of high dimensional data has no standard structure or schema, because of this problem has risen. This paper gives an overview of big data sources, challenges, scope and unstructured data mining techniques that can be used for big data

Towards a Blockchain Assisted Patient Owned System for Electronic Health Records

Security and privacy of patients’ data is a major concern in the healthcare industry. In this paper, we propose a system that activates robust security and privacy of patients’ medical records as well as enables interoperability and data exchange between the different healthcare providers. The work proposes the shift from patient’s electronic health records being managed and controlled by the healthcare industry to a patient-centric application where patients are in control of their data. The aim of this research is to build an Electronic Healthcare Record (EHR) system that is layered on the Ethereum blockchain platform and smart contract in order to eliminate the need for third-party systems. With this system, the healthcare provider can search for patient’s data and request the patients’ consent to access it. Patients manage their data which enables an expedited data exchange across EHR systems. Each patient’s data are stored on the peer-to-peer node ledger. The proposed patient-centric EHR platform is cross-platform compliant, as it can be accessed via personal computers and mobile devices and facilitates interoperability across healthcare providers as patients’ medical records are gathered from different healthcare providers and stored in a unified format. The proposed framework is tested on a private Ethereum network using Ganache. The results show the effectiveness of the system with respect to security, privacy, performance and interoperability

Enhancing IoT Data Dependability through a Blockchain Mirror Model

The Internet of Things (IoT) is a remarkable data producer and these data may be used to prevent or detect security vulnerabilities and increase productivity by the adoption of statistical and Artificial Intelligence (AI) techniques. However, these desirable benefits are gained if data from IoT networks are dependablethis is where blockchain comes into play. In fact, through blockchain, critical IoT data may be trusted, i.e., considered valid for any subsequent processing. A simple formal model named the Mirror Model is proposed to connect IoT data organized in traditional models to assets of trust in a blockchain. The Mirror Model sets some formal conditions to produce trusted data that remain trusted over time. A possible practical implementation of an application programming interface (API) is proposed, which keeps the data and the trust model in synch. Finally, it is noted that the Mirror Model enforces a top-down approach from reality to implementation instead of going the opposite way as it is now the practice when referring to blockchain and the IoT

Model-based integration testing technique using formal finite state behavioral models for component-based software

Many issues and challenges could be identified when considering integration testing of Component-Based Software Systems (CBSS). Consequently, several research have appeared in the literature, aimed at facilitating the integration testing of CBSS. Unfortunately, they suffer from a number of drawbacks and limitations such as difficulty of understanding and describing the behavior of integrated components, lack of effective formalism for test information, difficulty of analyzing and validating the integrated components, and exposing the components implementation by providing semi-formal models. Hence, these problems have made it in effective to test today’s modern complex CBSS. To address these problems, a model-based approach such as Model-Based Testing (MBT) tends to be a suitable mechanism and could be a potential solution to be applied in the context of integration testing of CBSS. Accordingly, this thesis presents a model-based integration testing technique for CBSS. Firstly, a method to extract the formal finite state behavioral models of integrated software components using Mealy machine models was developed. The extracted formal models were used to detect faulty interactions (integration bugs) or compositional problems between integrated components in the system. Based on the experimental results, the proposed method had significant impact in reducing the number of output queries required to extract the formal models of integrated software components and its performance was 50% better compared to the existing methods. Secondly, based on the extracted formal models, an effective model-based integration testing technique (MITT) for CBSS was developed. Finally, the effectiveness of the MITT was demonstrated by employing it in the air gourmet and elevator case studies, using three evaluation parameters. The experimental results showed that the MITT was effective and outperformed Shahbaz technique on the air gourmet and elevator case studies. In terms of learned components for air gourmet and elevator case studies respectively, the MITT results were better by 98.14% and 100%, output queries based on performance were 42.13% and 25.01%, and error detection capabilities were 70.62% and 75% for each of the case study

On Evaluating Commercial Cloud Services: A Systematic Review

Background: Cloud Computing is increasingly booming in industry with many competing providers and services. Accordingly, evaluation of commercial Cloud services is necessary. However, the existing evaluation studies are relatively chaotic. There exists tremendous confusion and gap between practices and theory about Cloud services evaluation. Aim: To facilitate relieving the aforementioned chaos, this work aims to synthesize the existing evaluation implementations to outline the state-of-the-practice and also identify research opportunities in Cloud services evaluation. Method: Based on a conceptual evaluation model comprising six steps, the Systematic Literature Review (SLR) method was employed to collect relevant evidence to investigate the Cloud services evaluation step by step. Results: This SLR identified 82 relevant evaluation studies. The overall data collected from these studies essentially represent the current practical landscape of implementing Cloud services evaluation, and in turn can be reused to facilitate future evaluation work. Conclusions: Evaluation of commercial Cloud services has become a world-wide research topic. Some of the findings of this SLR identify several research gaps in the area of Cloud services evaluation (e.g., the Elasticity and Security evaluation of commercial Cloud services could be a long-term challenge), while some other findings suggest the trend of applying commercial Cloud services (e.g., compared with PaaS, IaaS seems more suitable for customers and is particularly important in industry). This SLR study itself also confirms some previous experiences and reveals new Evidence-Based Software Engineering (EBSE) lessons