Keyword search for building service-based systems
- Authors: He, Qiang , Zhou, Rui , Zhang, Xuyun , Wang, Yanchun , Ye, Dayong , Chen, Feifei , Grundy, John , Yang, Yun
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE Transactions on Software Engineering Vol. 43, no. 7 (2017), p. 658-674
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- Description: With the fast growth of applications of service-oriented architecture (SOA) in software engineering, there has been a rapid increase in demand for building service-based systems (SBSs) by composing existing Web services. Finding appropriate component services to compose is a key step in the SBS engineering process. Existing approaches require that system engineers have detailed knowledge of SOA techniques which is often too demanding. To address this issue, we propose Keyword Search for Service-based Systems (KS3), a novel approach that integrates and automates the system planning, service discovery and service selection operations for building SBSs based on keyword search. KS3 assists system engineers without detailed knowledge of SOA techniques in searching for component services to build SBSs by typing a few keywords that represent the tasks of the SBSs with quality constraints and optimisation goals for system quality, e.g., reliability, throughput and cost. KS3 offers a new paradigm for SBS engineering that can significantly save the time and effort during the system engineering process. We conducted large-scale experiments using two real-world Web service datasets to demonstrate the practicality, effectiveness and efficiency of KS3. © 1976-2012 IEEE.
Hc-daf-2 encodes an insulin-like receptor kinase in the barber's pole worm, Haemonchus contortus, and restores partial dauer regulation
- Authors: Li, Facai , Lok, James , Gasser, Robin , Korhonen, Pasi , Sandeman, Mark , Shi, Deshi , Zhou, Rui , Li, Xiangrui , Zhou, Yanqin , Zhao, Junlong , Hu, Min
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal for Parasitology Vol. 44, no. 7 (2014), p. 485-496
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- Description: Infective L3s (iL3s) of parasitic nematodes share common behavioural, morphological and developmental characteristics with the developmentally arrested (dauer) larvae of the free-living nematode Caenorhabditis elegans. It is proposed that similar molecular mechanisms regulate entry into or exit from the dauer stage in C. elegans, and the transition from free-living to parasitic forms of parasitic nematodes. In C. elegans, one of the key factors regulating the dauer transition is the insulin-like receptor (designated Ce-DAF-2) encoded by the gene Ce-daf-2. However, nothing is known about DAF-2 homologues in most parasitic nematodes. Here, using a PCR-based approach, we identified and characterised a gene (Hc-daf-2) and its inferred product (Hc-DAF-2) in Haemonchus contortus (a socioeconomically important parasitic nematode of ruminants). The sequence of Hc-DAF-2 displays significant sequence homology to insulin receptors (IR) in both vertebrates and invertebrates, and contains conserved structural domains. A sequence encoding an important proteolytic motif (RKRR) identified in the predicted peptide sequence of Hc-DAF-2 is consistent with that of the human IR, suggesting that it is involved in the formation of the IR complex. The Hc-daf-2 gene was transcribed in all life stages of H. contortus, with a significant up-regulation in the iL3 compared with other stages. To compare patterns of expression between Hc-daf-2 and Ce-daf-2, reporter constructs fusing the Ce-daf-2 or Hc-daf-2 promoter to sequence encoding GFP were microinjected into the N2 strain of C. elegans, and transgenic lines were established and examined. Both genes showed similar patterns of expression in amphidial (head) neurons, which relate to sensation and signal transduction. Further study by heterologous genetic complementation in a daf-2-deficient strain of C. elegans (CB1370) showed partial rescue of function by Hc-daf-2. Taken together, these findings provide a first insight into the roles of Hc-daf-2/. Hc-DAF-2 in the biology and development of H. contortus, particularly in the transition to parasitism. © 2014 Australian Society for Parasitology Inc.
XPloreRank: exploring XML data via you may also like queries
- Authors: Naseriparsa, Mehdi , Liu, Chengfei , Islam, Md Saiful , Zhou, Rui
- Date: 2019
- Type: Text , Journal article
- Relation: World Wide Web Vol. 22, no. 4 (2019), p. 1727-1750
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- Description: In many cases, users are not familiar with their exact information needs while searching complicated data sources. This lack of understanding may cause the users to feel dissatisfaction when the system retrieves insufficient results after they issue queries. However, using their original query results, we may recommend additional queries which are highly relevant to the original query. This paper presents XPloreRank to recommend top-l highly relevant keyword queries called “You May Also Like” (YMAL) queries to the users in XML keyword search. To generate such queries, we firstly analyze the original keyword query results content and construct a weighted co-occurring keyword graph. Then, we generate the YMAL queries by traversing the co-occurring keyword graph and rank them based on the following correlation aspects: (a) external correlation, which measures the similarity of the YMAL query to the original query and (b) internal correlation, which measures the capability of the YMAL query keywords in producing meaningful results with respect to the data source. Due to the complexity of generating YMAL queries, we propose a novel A* search-based technique to generate top-l YMAL queries efficiently. We also present a greedy-based approximation for it to improve the performance further. Extensive experiments verify the effectiveness and efficiency of our approach. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.
A framework for processing cumulative frequency queries over medical data streams
- Authors: Al-Shammari, Ahmed , Zhou, Rui , Liu, Chengfei , Naseriparsa, Mehdi , Vo, Bao
- Date: 2018
- Type: Text , Conference paper
- Relation: 19th International Conference on Web Information Systems Engineering, WISE 2018 Vol. 11234 LNCS, p. 121-131
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- Description: Medical data streams processing becomes increasingly important since it extracts critical information from a continuous flow of patient data. Various types of problems have been studied on medical data streams, such as classification, clustering, anomaly detection, etc.; however, efficient evaluation of cumulative frequency queries has not been well studied. The cumulative frequency of patients’ status can play an instrumental role in monitoring the patients’ health conditions. Up to now, efficiently processing cumulative frequency queries on medical data streams is still a challenging task due to the large size of the incoming data. Therefore, in this paper, we propose a novel framework for processing the cumulative frequency queries over medical data streams to support the online medical decision. The proposed framework includes two components: data summarisation and dynamic maintenance. For data summarisation, we propose a hybrid approach that combines two data structures and exploits a classification algorithm to select the more efficient data structure for computing the cumulative frequency. For dynamic maintenance, we propose an incremental maintenance approach for updating the cumulative frequencies when new data arrive. The experimental results on a real dataset demonstrate the efficiency of the proposed approach. © Springer Nature Switzerland AG 2018.
An effective density-based clustering and dynamic maintenance framework for evolving medical data streams
- Authors: Al-Shammari, Ahmed , Zhou, Rui , Naseriparsaa, Mehdi , Liu, Chengfei
- Date: 2019
- Type: Text , Journal article
- Relation: International Journal of Medical Informatics Vol. 126, no. (2019), p. 176-186
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- Description: Background: Medical data stream clustering has become an integral part of medical decision systems since it extracts highly-sensitive information from a tremendous flow of medical data. However, clustering and maintaining of medical data streams is still a challenging task. That is because the evolving of medical data streams imposes various challenges for clustering such as the ability to discover the arbitrary shape of a cluster, the ability to group data streams without a predefined number of clusters, and the ability to maintain the data clusters dynamically. Objective: To support the online medical decisions, there is a need to address the clustering challenges. Therefore, in this paper, we propose an effective density-based clustering and dynamic maintenance framework for grouping the patients with similar symptoms into meaningful clusters and monitoring the patients’ status frequently. Methods: For clustering, we generate a set of initial medical data clusters based on the combination of Piece-wise Aggregate Approximation and the density-based spatial clustering of applications with noise called (PAA+DBSCAN) algorithm. For maintenance, when new medical data streams arrive, we maintain the initially generated medical data clusters dynamically. Since the incremental cluster maintenance is time-consuming, we further propose an Advanced Cluster Maintenance (ACM) approach to improve the performance of the dynamic cluster maintenance. Results: The experimental results on real-world medical datasets demonstrate the effectiveness and efficiency of our proposed approaches. The PAA+DBSCAN algorithm is more efficient and effective than the exact DBSCAN algorithm. Moreover, the ACM approach requires less running time in comparison with the Baseline Cluster Maintenance (BCM) approach using different tuning parameter values in all datasets. That is because the BCM approach tracks all the data points in the cluster. Conclusion: The proposed framework is capable of clustering and maintaining the medical data streams effectively by means of grouping the patients who share similar symptoms and tracking the patients status that naturally tends to be changing over time. © 2019 Elsevier B.V.