With the ever-increasing prevalence of web APIs (Application Programming Interfaces) in enabling smart software developments, finding and composing a list of existing web APIs that can corporately fulfil the software developers' functional needs have become a promising way to develop a successful mobile app, economically and conveniently. However, the big volume and diversity of candidate web APIs put additional burden on the app developers' web APIs selection decision-makings, since it is often a challenging task to simultaneously guarantee the diversity and compatibility of the finally selected a set of web APIs. Considering this challenge, a Diversity-aware and Compatibility-driven web APIs Recommendation approach, namely DivCAR, is put forward in this paper. First, to achieve diversity, DivCAR employs random walk sampling technique on a pre-built correlation graph to generate diverse correlation subgraphs. Afterwards, with the diverse correlation subgraphs, we model the compatible web APIs recommendation problem to be a minimum group Steiner tree search problem. Through solving the minimum group Steiner tree search problem, manifold sets of compatible and diverse web APIs ranked are returned to the app developers. At last, we design and enact a set of experiments on a real-world dataset crawled from www.programmableWeb.com. Experimental results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the web APIs recommendation diversity and compatibility.
Knowledge graphs (KGs) on COVID-19 have been constructed to accelerate the research process of COVID-19. However, KGs are always incomplete, especially the new constructed COVID-19 KGs. Link prediction task aims to predict missing entities for (e, r, t) or (h, r, e), where $h$ and $t$ are certain entities, $e$ is an entity that needs to be predicted and $r$ is a relation. This task also has the potential to solve COVID-19 related KGs' incomplete problem. Although various knowledge graph embedding (KGE) approaches have been proposed to the link prediction task, these existing methods suffer from the limitation of using a single scoring function, which fails to capture rich features of COVID-19 KGs. In this work, we propose the MDistMult model that leverages multiple scoring functions to extract more features from existing triples. We employ experiments on the CCKS2020 COVID-19 Antiviral Drugs Knowledge Graph (CADKG). The experimental results demonstrate that our MDistMult achieves state-of-the-art performance in link prediction task on the CADKG dataset.
2017 International Conference on Data Mining, Communications and Information Technology (DMCIT 2017) took place in Phuket on 25-27 May, 2017. The conference provides a useful platform both for showing the latest research directions and for exchange of research results and ideas in broad Data Mining, Communications and Information Technology areas. The participants of the conferences were from many parts of the world, with backgrounds in academia and industry. The success of the conferences is reflected by high quality of the papers received.
Meaningfully automating sociotechnical business collaboration promises efficiency-, effectiveness-, and quality increases for realizing next-generation decentralized autonomous organizations. For automating business-process aware cross-organizational operations, the development of existing choreography languages is technology driven and focuses less on sociotechnical suitability and expressiveness concepts and properties that recognize the interaction between people in organizations and technology in workplaces. This gap our suitability- and expressiveness exploration fills by means of a cross-organizational collaboration ontology that we map as a proof-of-concept evaluation to the eSourcing Markup Language (eSML). The latter we test in a feasibility case study to meaningfully support the automation of business collaboration. The developed eSourcing ontology and eSML is replicable for exploring strengths and weaknesses of other choreography languages.
With the normalization of epidemic prevention, many hotels regard pre-sale as a "life-saving straw", but it leaves a "sequelae" of booking cancellation. Therefore, based on risk aversion theory and attribution theory, this paper studies the influencing factors of hotel booking cancellation behavior of consumers through situational experiment. Results show that: (1) Electronic word-of-mouth dispersion has a significant positive impact on booking cancellation behavior of hotel consumers. (2) Attribution selection can mediate the influence of hotel electronic word-of-mouth dispersion on booking cancellation behavior of consumers. (3) Self-construal can moderate the influence of electronic word-of-mouth dispersion on attribution selection. Findings explore the important factor that influence the booking cancellation behavior of hotel consumers, and provides theoretical guidance and reference for the management of hotel booking cancellation phenomenon.
MapReduce, a large-scale data processing paradigm, is gaining popularity.However, like other distributed computing frameworks, MapReduce suffers from the integrity assurance vulnerability: malicious workers in the MapReduce cluster could tamper with its computation result and thereby render the overall computation result inaccurate.Existing solutions are effective in defeating the malicious behavior of non-collusive workers, but are less effective in detecting collusive workers.In this paper, we propose the Verification-based Integrity Assurance Framework (VIAF).By using task replication and probabilistic result verification, VIAF can detect both non-collusive and collusive workers, even if the malicious workers dominate the environment.We have implemented VIAF on Hadoop, an open source MapReduce implementation.Our theoretical analysis and experimental result show that VIAF can achieve high job accuracy while imposing moderate performance overhead.
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