METHOD AND DEVICE FOR AUTOMATED JOB FAILOVER

In an embodiment of the present disclosure, there is provided a method of managing multi data cluster failure. The method comprises scheduling one or more applications on a primary cluster and a secondary cluster, monitoring a status of the one or more applications scheduled in one cluster by the other data cluster, identifying the status of the applications of one cluster by the other cluster, updating the status of the one or more applications associated with each cluster on the distributed file system (DFS) of respective cluster, and generating output reports of the scheduled one or more applications. The method automatically converts the secondary data cluster to the primary cluster and vice-versa upon identifying the status of primary cluster as under failure or under maintenance during monitoring

Private search over big data leveraging distributed file system and parallel processing

In this work, we identify the security and privacy problems associated with a certain Big Data application, namely secure keyword-based search over encrypted cloud data and emphasize the actual challenges and technical difficulties in the Big Data setting. More specifically, we provide definitions from which privacy requirements can be derived. In addition, we adapt an existing work on privacy-preserving keyword-based search method to the Big Data setting, in which, not only data is huge but also changing and accumulating very fast. Our proposal is scalable in the sense that it can leverage distributed file systems and parallel programming techniques such as the Hadoop Distributed File System (HDFS) and the MapReduce programming model, to work with very large data sets. We also propose a lazy idf-updating method that can efficiently handle the relevancy scores of the documents in a dynamically changing, large data set. We empirically show the efficiency and accuracy of the method through extensive set of experiments on real data

Obfuscation-based malware update: A comparison of manual and automated methods

Indexación: Scopus; Web of Science.This research presents a proposal of malware classification and its update based on capacity and obfuscation. This article is an extension of [4]a, and describes the procedure for malware updating, that is, to take obsolete malware that is already detectable by antiviruses, update it through obfuscation techniques and thus making it undetectable again. As the updating of malware is generally performed manually, an automatic solution is presented together with a comparison from the standpoint of cost and processing time. The automated method proved to be more reliable, fast and less intensive in the use of resources, specially in terms of antivirus analysis and malware functionality checking times.http://univagora.ro/jour/index.php/ijccc/article/view/2961/112

Continuous Opinions and Discrete Actions in Opinion Dynamics Problems

A model where agents show discrete behavior regarding their actions, but have continuous opinions that are updated by interacting with other agents is presented. This new updating rule is applied to both the voter and Sznajd models for interaction between neighbors and its consequences are discussed. The appearance of extremists is naturally observed and it seems to be a characteristic of this model.Comment: 10 pages, 4 figures, minor changes for improved clarit

Assessment of a method to detect signals for updating systematic reviews.

BackgroundSystematic reviews are a cornerstone of evidence-based medicine but are useful only if up-to-date. Methods for detecting signals of when a systematic review needs updating have face validity, but no proposed method has had an assessment of predictive validity performed.MethodsThe AHRQ Comparative Effectiveness Review program had produced 13 comparative effectiveness reviews (CERs), a subcategory of systematic reviews, by 2009, 11 of which were assessed in 2009 using a surveillance system to determine the degree to which individual conclusions were out of date and to assign a priority for updating each report. Four CERs were judged to be a high priority for updating, four CERs were judged to be medium priority for updating, and three CERs were judged to be low priority for updating. AHRQ then commissioned full update reviews for 9 of these 11 CERs. Where possible, we matched the original conclusions with their corresponding conclusions in the update reports, and compared the congruence between these pairs with our original predictions about which conclusions in each CER remained valid. We then classified the concordance of each pair as good, fair, or poor. We also made a summary determination of the priority for updating each CER based on the actual changes in conclusions in the updated report, and compared these determinations with the earlier assessments of priority.ResultsThe 9 CERs included 149 individual conclusions, 84% with matches in the update reports. Across reports, 83% of matched conclusions had good concordance, and 99% had good or fair concordance. The one instance of poor concordance was partially attributable to the publication of new evidence after the surveillance signal searches had been done. Both CERs originally judged as being low priority for updating had no substantive changes to their conclusions in the actual updated report. The agreement on overall priority for updating between prediction and actual changes to conclusions was Kappa = 0.74.ConclusionsThese results provide some support for the validity of a surveillance system for detecting signals indicating when a systematic review needs updating

Ontobull and BFOConvert: Web-based programs to support automatic ontology conversion

When a widely reused ontology appears in a new version which is not compatible with older versions, the ontologies reusing it need to be updated accordingly. Ontobull has been developed to automatically update ontologies with new term IRI(s) and associated metadata to take account of such version changes. To use the Ontobull web interface a user is required to (i) upload one or more ontology OWL source files; (ii) input an ontology term IRI mapping; and (where needed) (iii) provide update settings for ontology headers and XML namespace IDs. Using this information, the backend Ontobull Java program automatically updates the OWL ontology files with desired term IRIs and ontology metadata. The Ontobull subprogram BFOConvert supports the conversion of an ontology that imports a previous version of BFO. A use case is provided to demonstrate the features of Ontobull and BFOConvert