Provenance 기술 동향 분석

비매

A Privacy Protected k-NN Query Processing Algorithm Based on Network Voronoi Diagram in Spatial Networks

With the advances in wireless internet and mobile positioning technology, location-based services (LBSs) become popular. In LBS, users must send their exact locations to enjoy the services while they may cause several privacy threats. To solve this problem, query processing algorithms based on a cloaking method have been proposed. The algorithms use spatial cloaking methods to blur the user’s exact location into a region satisfying the required privacy threshold (k). With the cloaked region, a LBS server can execute a spatial query processing algorithm preserving their privacy. However, the existing algorithms cannot provide good query processing performance. To resolve this problem, we, in this paper, propose a k-NN query processing algorithm based on network Voronoi diagram for spatial networks. Therefore, our algorithm can reduce network expansion overhead and share the information of the expanded road network. In order to demonstrate the efficiency of our algorithms, we have conducted extensive performance evaluations. The results show that our algorithm achieves better performance on retrieval time than the existing algorithms, such as PSNN and kRNN. This is because our k-NN query processing algorithm can greatly reduce a network expansion cost for retrieving k POIs

k-Nearest Neighbor Query Processing Algorithm for Cloaking Regions

Duetotheadvancementofwirelessinternetandmobilepositioningtechnology,theapplicationoflocation-basedservices(LBSs)hasbecomepopularformobileusers.Sinceusershavetosendtheirexactlocationstoobtaintheservice,itmayleadtoseveralprivacythreats.Tosolvethisproblem,acloakingmethodhasbeenproposedtoblurusers’exactlocationsintoacloakedspatialregionwitharequiredprivacythreshold(k).Withthecloakedregion,anLBSservercancarryoutak-nearestneighbor(k-NN)searchalgorithm.Somerecentstudieshaveproposedmethodstosearchk-nearestPOIswhileprotectingauser’sprivacy.However,theyhaveatleastonemajorproblem,suchasinefficiencyonqueryprocessingorlowprecisionofretrievedresult.Toresolvetheseproblems,inthispaper,weproposeanovelk-NNqueryprocessingalgorithmforacloakingregiontosatisfybothrequirementsoffastqueryprocessingtimeandhighprecisionoftheretrievedresult.Toachievefastqueryprocessingtime,weproposeanewpruningtechniquebasedona2D-coodinatescheme.Inaddition,wemakeuseofaVoronoidiagramforretrievingthenearestPOIsefficiently.Tosatisfytherequirementofhighprecisionoftheretrievedresult,weguaranteethatourk-NNqueryprocessingalgorithmalwayscontainstheexactsetofknearestneighbors.OurperformanceanalysisshowsthatouralgorithmachievesbetterperformanceintermsofqueryprocessingtimeandthenumberofcandidatePOIscomparedwithotheralgorithms

Platform to Build the Knowledge Base by Combining Sensor Data and Context Data

Sensor data is structured and generally lacks of meaning by itself, but life-logging data (time, location, etc.) out of sensor data can be utilized to create lots of meaningful information combined with social data from social networks like Facebook and Twitter. There have been many platforms to produce meaningful information and support human behavior and context-awareness through integrating diverse mobile, social, and sensing input streams. The problem is that these platforms don’t guarantee the performance in terms of the processing time and even let the accuracy of output data be addressed by new studies in each area where the platform is applied. Thus, this study proposes an improved platform which builds a knowledge base for context awareness by applying distributed and parallel computing approach considering the characteristics of sensor data that is collected and processed in real-time, and compares the proposed platform with existing platforms in terms of performance. The experiment shows the proposed platform is an advanced platform in terms of processing time. We reduce the processing time by 40% compared with existing platform. The proposed platform also guarantees the accuracy compared with existing platform

Semantic complex event processing model for reasoning research activities

With recent developments in science and technology, numerous researchers around the world are producing a variety of science- and technology-related documents such as research papers and patents in order to share and further develop their respective research.  In particular, the spread of social network services is allowing researchers in science and technology to share and develop their latest technologies at a fast rate.  In the era of Big Data, there are increasing needs for a system that can infer analytical information about the research activities of such researchers, but no system that satisfies the requirements exists at the moment.  Therefore, this study proposes a sematic complex event processing model for reasoning.  The study then details an architecture in which such analytics is processed on a real-time basis in a distributed environment.  With this architecture, researchers can easily monitor their individual research activities in an up to date manner and use such analytical data as a basis to set their future research directions