Sound Multi-Party Business Protocols for Service Networks

Service networks comprise large numbers of long-running, highly dynamic complex end-to-end service interactions reflecting asynchronous message flows that typically transcend several organizations and span several geographical locations. At the communication level, service network business protocols can be flexible ranging from conventional inter-organizational point-to-point service interactions to fully blown dynamic multi-party interactions of global reach within which each participant may contribute its activities and services. In this paper we introduce a formal framework enriched with temporal constraints to describe multiparty business protocols for service networks. We extend this framework with the notion of multi-party business protocol soundness and show how it is possible to execute a multi-party protocol consistently in a completely distributed manner while guaranteeing eventual termination

Software expert discovery via knowledge domain embeddings in a collaborative network

© 2018 Elsevier B.V. Community Question Answering (CQA) websites can be claimed as the most major venues for knowledge sharing, and the most effective way of exchanging knowledge at present. Considering that massive amount of users are participating online and generating huge amount data, management of knowledge here systematically can be challenging. Expert recommendation is one of the major challenges, as it highlights users in CQA with potential expertise, which may help match unresolved questions with existing high quality answers while at the same time may help external services like human resource systems as another reference to evaluate their candidates. In this paper, we in this work we propose to exploring experts in CQA websites. We take advantage of recent distributed word representation technology to help summarize text chunks, and in a semantic view exploiting the relationships between natural language phrases to extract latent knowledge domains. By domains, the users’ expertise is determined on their historical performance, and a rank can be compute to given recommendation accordingly. In particular, Stack Overflow is chosen as our dataset to test and evaluate our work, where inclusive experiment shows our competence

Truth discovery via exploiting implications from multi-source data

Data veracity is a grand challenge for various tasks on the Web. Since the web data sources are inherently unreliable and may provide con icting information about the same real-world entities, truth discovery is emerging as a counter- measure of resolving the con icts by discovering the truth, which conforms to the reality, from the multi-source data. A major challenge related to truth discovery is that different data items may have varying numbers of true values (or multi-truth), which counters the assumption of existing truth discovery methods that each data item should have exactly one true value. In this paper, we address this challenge by exploiting and leveraging the implications from multi-source data. In particular, we exploit three types of implications, namely the implicit negative claims, the distribution of positive/negative claims, and the co-occurrence of values in sources' claims, to facilitate multi-truth discovery. We propose a probabilistic approach with improvement measures that incorporate the three implications in all stages of truth discovery process. In particular, incorporating the negative claims enables multi-truth discovery, considering the distribution of positive/negative claims relieves truth discovery from the impact of sources' behavioral features in the specific datasets, and considering values' co-occurrence relationship compensates the information lost from evaluating each value in the same claims individually. Experimental results on three real-world datasets demonstrate the effectiveness of our approach.Xianzhi Wang, Quan Z. Sheng, Lina Yao, Xue Li, Xiu Susie Fang, Xiaofei Xu, and Boualem Benatalla

Empowering truth discovery with multi-truth prediction

Truth discovery is the problem of detecting true values from the con icting data provided by multiple sources on the same data items. Since sources' reliability is unknown a priori, a truth discovery method usually estimates sources' reliability along with the truth discovery process. A major limitation of existing truth discovery methods is that they commonly assume exactly one true value on each data item and therefore cannot deal with the more general case that a data item may have multiple true values (or multi-truth). Since the number of true values may vary from data item to data item, this requires truth discovery methods being able to detect varying numbers of truth values from the multi source data. In this paper, we propose a multi-truth discovery approach, which addresses the above challenges by providing a generic framework for enhancing existing truth discovery methods. In particular, we redeem the numbers of true values as an important clue for facilitating multi-truth discovery. We present the procedure and components of our approach, and propose three models, namely the byproduct model, the joint model, and the synthesis model to implement our approach. We further propose two extensions to enhance our approach, by leveraging the implications of similar numerical values and values' co-occurrence informa- tion in sources' claims to improve the truth discovery accuracy. Experimental studies on real-world datasets demonstrate the effectiveness of our approach.Xianzhi Wang, Quan Z. Sheng, Lina Yao, Xue Li, Xiu Susie Fang, Xiaofei Xu, and Boualem Benatalla

Modeling Web Services by Iterative Reformulation of Functional and Non-Functional Requirements

Abstract. We propose an approach for incremental modeling of composite Web services. The technique takes into consideration both the functional and nonfunctional requirements of the composition. While the functional requirements are described using symbolic transition systems—transition systems augmented with state variables, function invocations, and guards; non-functional requirements are quantified using thresholds. The approach allows users to specify an abstract and possibly incomplete specification of the desired service (goal) that can be realized by selecting and composing a set of pre-existing services. In the event that such a composition is unrealizable, i.e. the composition is not functionally equivalent to the goal or the non-functional requirements are violated, our system provides the user with the causes for the failure, that can be used to appropriately reformulate the functional and/or non-functional requirements of the goal specification.