Due to the pervasiveness of incomplete data, incomplete data queries are vital in a large number of real-life scenarios. Current models and approaches for incomplete data queries mainly rely on the machine power. In this paper, we study the problem ofskyline queries over incomplete data with crowdsourcing. We propose a novel query framework, termed as${\sf BayesCrowd}$, which takes into account the data correlation using the Bayesian network. We leverage the typicalc-tablemodel on incomplete data to represent objects. Considering budget and latency constraints, we present a suite of effective task selection strategies. Moreover, we introduce amarginal utilityfunction to measure the benefit of crowdsourcing one task. In particular, the probability computation of each object being an answer object is at least as hard as #SAT problem. To this end, we propose anadaptiveDPLL (i.e., Davis-Putnam-Logemann- Loveland) algorithm to speed up the computation. Extensive experiments using both real and synthetic data sets confirm the superiority of${\sf BayesCrowd}$to the state-of-the-art method, in terms of execution time, monetary cost, and latency minimization.

Due to the pervasiveness of incomplete data, incomplete data queries are vital in a large number of real-life scenarios. Current models and approaches for incomplete data queries mainly rely on the machine power. In this paper, we study the problem of skyline queries over incomplete data with crowdsourcing. We propose a novel query framework, termed as BayesCrowd, on top of Bayesian network and the typical c-table model on incomplete data. Considering budget and latency constraints, we present a suite of effective task selection strategies. In particular, since the probability computation of each object being an answer object is at least as hard as #SAT problem, we propose an adaptive DPLL (i.e., Davis-Putnam-Logemann-Loveland) algorithm to speed up the computation. Extensive experiments using both real and synthetic data sets confirm the superiority of BayesCrowd to the state-of-the-art method.

The range-based skyline (r-skyline) query on road networks retrieves the skyline objects of taking each point within a road region as a query point, in terms of objects' spatial and non-spatial attributes. In this paper, we systematically carry out the study of why-not questions on the r-skyline query in the road network (abbreviated as the why-not RSQ problem). We present three modification strategies, including modifying the query range, modifying the why-not point, and modifying both of them, for the why-not RSQ problem. In particular, a suite of newly presented effective concepts/techniques are leveraged, such as the concepts of skyline scope and skyline dominance region, non-spatial attribute modification pruning, and G-tree index. Extensive experiments using both real and synthetic data sets demonstrate the performance of our proposed algorithms.

The range-based skyline (r-skyline) query on road networks retrieves the skyline objects for each of the query points that are within a road region, considering the objects' spatial and non-spatial attributes. However, reasoning about missing query results, specified by why-not questions, has not till recently received the attention it is worth of. In this paper, we systematically carry out the study of why-not questions on the r-skyline query in the road network environment (abbrev. as the why-not RSQ problem). We present three modification strategies, including modifying the query range, modifying the why-not point, and modifying both of them, for supporting the why-not RSQ problem. We also propose three efficient algorithms to tackle the why-not RSQ problem, where several newly presented effective concepts/techniques are leveraged, such as the concepts of skyline scope and skyline dominance region, non-spatial attribute modification pruning, and G-tree index. Extensive experimental evaluation using both real and synthetic data sets demonstrates the performance of our proposed algorithms.

In this paper, two multifault diagnosis methods based on improved support vector machine (SVM) are proposed for sensor fault detection and identification respectively. First, online sparse least squares support vector machine (OS-LSSVM) is utilized to detect and predict sensor faults. Then, a method which combines the SVM and error-correcting output codes (ECOC) called ECOC-SVM is proposed to solve the sensor fault feature extraction and online identification problem. We regard nonlinear transformation as the input of classifiers to enhance the separability of initial characteristics. ECOC-SVM is utilized to classify the fault states. Some typical faults are investigated and the experimental results indicate that ECOC-SVM has high identification accuracy and can be implemented in real-time to meet the requirements of online fault identification. This method can also be extended to solve other related problems.

There are thousands of strongly conserved non-coding elements (CNEs) in vertebrate genomes, and their functions remain largely unknown. However, without biologically relevant criteria for prioritizing them, selecting a particular CNE sequences to study can be haphazard. To address this problem, we present cneViewer-a database and webtool that systematizes information on conserved non-coding DNA elements in zebrafish. A key feature here is the ability to search for CNEs that may be relevant to tissue-specific gene regulation, based on known developmental expression patterns of nearby genes. cneViewer provides this and other organizing features that significantly facilitate experimental design and CNE analysis.