BackgroundSynthesis of multiple randomized controlled trials (RCTs) in a systematic review can summarize the effects of individual outcomes and provide numerical answers about the effectiveness of interventions. Filtering of searches is time consuming, and no single method fulfills the principal requirements of speed with accuracy. Automation of systematic reviews is driven by a necessity to expedite the availability of current best evidence for policy and clinical decision-making.We developed Rayyan (http://rayyan.qcri.org), a free web and mobile app, that helps expedite the initial screening of abstracts and titles using a process of semi-automation while incorporating a high level of usability. For the beta testing phase, we used two published Cochrane reviews in which included studies had been selected manually. Their searches, with 1030 records and 273 records, were uploaded to Rayyan. Different features of Rayyan were tested using these two reviews. We also conducted a survey of Rayyan’s users and collected feedback through a built-in feature.ResultsPilot testing of Rayyan focused on usability, accuracy against manual methods, and the added value of the prediction feature. The “taster” review (273 records) allowed a quick overview of Rayyan for early comments on usability. The second review (1030 records) required several iterations to identify the previously identified 11 trials. The “suggestions” and “hints,” based on the “prediction model,” appeared as testing progressed beyond five included studies. Post rollout user experiences and a reflexive response by the developers enabled real-time modifications and improvements. The survey respondents reported 40% average time savings when using Rayyan compared to others tools, with 34% of the respondents reporting more than 50% time savings. In addition, around 75% of the respondents mentioned that screening and labeling studies as well as collaborating on reviews to be the two most important features of Rayyan.As of November 2016, Rayyan users exceed 2000 from over 60 countries conducting hundreds of reviews totaling more than 1.6M citations. Feedback from users, obtained mostly through the app web site and a recent survey, has highlighted the ease in exploration of searches, the time saved, and simplicity in sharing and comparing include-exclude decisions. The strongest features of the app, identified and reported in user feedback, were its ability to help in screening and collaboration as well as the time savings it affords to users.ConclusionsRayyan is responsive and intuitive in use with significant potential to lighten the load of reviewers.

Abstract. Ranking queries, also known as top-k queries, produce results that are ordered on some computed score. Typically, these queries involve joins, where users are usually interested only in the top-k join results. Top-k queries are dominant in many emerging applications, e.g., multimedia retrieval by content, Web databases, data mining, middlewares, and most information retrieval applications. Current relational query processors do not handle ranking queries efficiently, especially when joins are involved. In this paper, we address supporting top-k join queries in relational query processors. We introduce a new rank-join algorithm that makes use of the individual orders of its inputs to produce join results ordered on a user-specified scoring function. The idea is to rank the join results progressively during the join operation. We introduce two physical query operators based on variants of ripple join that implement the rank-join algorithm. The operators are nonblocking and can be integrated into pipelined execution plans. We also propose an efficient heuristic designed to optimize a top-k join query by choosing the best join order. We address several practical issues and optimization heuristics to integrate the new join operators in practical query processors. We implement the new operators inside a prototype database engine based on PREDATOR. The experimental evaluation of our approach compares recent algorithms for joining ranked inputs and shows superior performance.

Abstract. Service composition is gaining momentum as the potential silver bullet for the envisioned Semantic Web. It purports to take the Web to unexplored efficiencies and provide a flexible approach for promoting all types of activities in tomorrow's Web. Applications expected to heavily take advantage of Web service composition include B2B E-commerce and E-government. To date, enabling composite services has largely been an ad hoc, time-consuming, and error-prone process involving repetitive low-level programming. In this paper, we propose an ontology-based framework for the automatic composition of Web services. We present a technique to generate composite services from high-level declarative descriptions. We define formal safeguards for meaningful composition through the use of composability rules. These rules compare the syntactic and semantic features of Web services to determine whether two services are composable. We provide an implementation using an E-government application offering customized services to indigent citizens. Finally, we present an exhaustive performance experiment to assess the scalability of our approach.

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Abstract

Large repositories of data contain sensitive information which must be protected against unauthorized access. The protection of the confidentiality of tills information has been a long-term goal for the database security research community and the government statistical agencies. Recent advances, in data mining and machine learning algorithms, have increased the disclosure risks one may encounter when releasing data to outside parties.

Abstract-Periodicity mining is used for predicting trends in time series data. Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. Existing periodicity mining algorithms assume that the periodicity rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in Oðn log nÞ time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered periodicity rates and periodic patterns. Real-data experiments demonstrate the practicality of the discovered periodic patterns.

In many business scenarios, record matching is performed across different data sources with the aim of identifying common information shared among these sources. However such need is often in contrast with privacy requirements concerning the data stored by the sources. In this paper, we propose a protocol for record matching that preserves privacy both at the data level and at the schema level. Specifically, if two sources need to identify their common data, by running the protocol they can compute the matching of their datasets without sharing their data in clear and only sharing the result of the matching.The protocol uses a third party, and maps records into a vector space in order to preserve their privacy.Experimental results show the efficiency of the matching protocol in terms of precision and recall as well as the good computational performance.