Bhuvan Bamba

Supporting anonymous location queries in mobile environments with privacygrid

This paper presents PrivacyGrid - a framework for supporting anonymous location-based queries in mobile information delivery systems. The PrivacyGrid framework offers three unique capabilities. First, it provides a location privacy protection preference profile model, called location P3P, which allows mobile users to explicitly define their preferred location privacy requirements in terms of both location hiding measures (e.g., location k-anonymity and location l-diversity) and location service quality measures (e.g., maximum spatial resolution and maximum temporal resolution). Second, it provides fast and effective location cloaking algorithms for location k-anonymity and location l-diversity in a mobile environment. We develop dynamic bottom-up and top-down grid cloaking algorithms with the goal of achieving high anonymization success rate and efficiency in terms of both time complexity and maintenance cost. A hybrid approach that carefully combines the strengths of both bottom-up and top-down cloaking approaches to further reduce the average anonymization time is also developed. Last but not the least, PrivacyGrid incorporates temporal cloaking into the location cloaking process to further increase the success rate of location anonymization. We also discuss PrivacyGrid mechanisms for supporting anonymous location queries. Experimental evaluation shows that the PrivacyGrid approach can provide close to optimal location k-anonymity as defined by per user location P3P without introducing significant performance penalties.

Coupled placement in modern data centers

We introduce the coupled placement problem for modern data centers spanning placement of application computation and data among available server and storage resources. While the two have traditionally been addressed independently in data centers, two modern trends make it beneficial to consider them together in a coupled manner: (a) rise in virtualization technologies, which enable applications packaged as VMs to be run on any server in the data center with spare compute resources, and (b) rise in multi-purpose hardware devices in the data center which provide compute resources of varying capabilities at different proximities from the storage nodes.

Information retrieval and knowledge discovery utilizing a biomedical patent semantic Web

Before undertaking new biomedical research, identifying concepts that have already been patented is essential. A traditional keyword-based search on patent databases may not be sufficient to retrieve all the relevant information, especially for the biomedical domain. This paper presents BioPatentMiner, a system that facilitates information retrieval and knowledge discovery from biomedical patents. The system first identifies biological terms and relations from the patents and then integrates the information from the patents with knowledge from biomedical ontologies to create a semantic Web. Besides keyword search and queries linking the properties specified by one or more RDF triples, the system can discover semantic associations between the Web resources. The system also determines the importance of the resources to rank the results of a search and prevent information overload while determining the semantic associations.

BioPatentMiner: an information retrieval system for biomedical patents

Before undertaking new biomedical research, identifying concepts that have already been patented is essential. Traditional keyword based search on patent databases may not be sufficient to retrieve all the relevant information, especially for the biomedical domain. More sophisticated retrieval techniques are required. This paper presents BioPatentMiner, a system that facilitates information retrieval from biomedical patents. It integrates information from the patents with knowledge from biomedical ontologies to create a Semantic Web. Besides keyword search and queries linking the properties specified by one or more RDF triples, the system can discover Semantic Associations between the resources. The system also determines the importance of the resources to rank the results of a search and prevent information overload while determining the Semantic Associations.

Utilizing resource importance for ranking semantic web query results

To realize the vision of the Semantic Web, effective techniques of Information Retrieval need to be developed. Ranking the results of a search is one of the main challenges of an Information Retrieval system. In this paper we present a technique for ranking the results of a Semantic Web query. The ranking is based on various factors including the Semantic Web resource importance. We have modified a World-wide Web link analysis technique that has been effectively used to identify important Web pages to calculate the importance of Semantic Web resources. Our ranking technique has been utilized for ranking the query results of a Biomedical Patent Semantic Web.

Distributed Processing of Spatial Alarms: A Safe Region-Based Approach

Spatial alarms are considered as one of the basic capabilities in future mobile computing systems for enabling personalization of location-based services. In this paper, we propose a distributed architecture and a suite of safe region techniques for scalable processing of spatial alarms. We show that safe region-based processing enables resource optimal distribution of partial alarm processing tasks from the server to the mobile clients. We propose three different safe region computation algorithms to explore the impact of size and shape of the safe region on network bandwidth, server load and client energy consumption. Concretely, we show that the maximum weighted perimeter rectangular safe region approach outperforms previous techniques in terms of performance and accuracy. We further explore finer granularity safe regions by introducing grid-based and pyramid-based representation of rectilinear polygonal shapes using bitmap encoding. Our experimental evaluation shows that the distributed safe region-based architecture outperforms the two most popular server-centric approaches, periodic and safe period-based, for spatial alarm processing.

RoadTrack: scaling location updates for mobile clients on road networks with query awareness

Mobile commerce and location based services (LBS) are some of the fastest growing IT industries in the last five years. Location update of mobile clients is a fundamental capability in mobile commerce and all types of LBS. Higher update frequency leads to higher accuracy, but incurs unacceptably high cost of location management at the location servers. We propose RoadTrack -- a road-network based, query-aware location update framework with two unique features. First, we introduce the concept of precincts to control the granularity of location update resolution for mobile clients that are not of interest to any active location query services. Second, we define query encounter points for mobile objects that are targets of active location query services, and utilize these encounter points to define the adequate location update schedule for each mobile. The RoadTrack framework offers three unique advantages. First, encounter points as a fundamental query awareness mechanism enable us to control and differentiate location update strategies for mobile clients in the vicinity of active location queries, while meeting the needs of location query evaluation. Second, we employ system-defined precincts to manage the desired spatial resolution of location updates for different mobile clients and to control the scope of query awareness to be capitalized by a location update strategy. Third, our road-network based check-free interval optimization further enhances the effectiveness of the Road-Track query-aware location update scheduling algorithm. This optimization provides significant cost reduction for location update management at both mobile clients and location servers. We evaluate the RoadTrack location update approach using a real world road-network based mobility simulator. Our experimental results demonstrate that the RoadTrack query aware location update approach outperforms existing representative location update strategies in terms of both client energy efficiency and server processing load.

Scalable processing of spatial alarms

Spatial alarms can be modeled as location-based triggerswhich are fired whenever the subscriber enters the spatial region aroundthe location of interest associated with the alarm. Alarm processing requiresmeeting two demanding objectives: high accuracy, which ensureszero or very low alarm misses, and system scalability, which requireshighly efficient processing of spatial alarms. Existing techniques like periodicevaluation or continuous query-based approach, when applied tothe spatial alarm processing problem, lead to unpredictable inaccuracyin alarm processing or unnecessarily high computational costs or both.In order to deal with these weaknesses, we introduce the concept ofsafe period to minimize the number of unnecessary spatial alarm evaluations,increasing the throughput and scalability of the server. Further,we develop alarm grouping techniques based on locality of the alarmsand motion behavior of the mobile users, which reduce safe period computationcosts at the server side. An evaluation of the scalability andaccuracy of our approach using a road network simulator shows that theproposed approach offers significant performance enhancements for thealarm processing server.

Topological relationship query processing for complex regions in Oracle Spatial

Although geographic information systems (GIS) and spatial database communities have extensively studied topological relationships for more than two decades, there is little literature describing how to efficiently implement them in GIS and spatial database systems. This is rather surprising considering that topological relationship queries are supported in many GIS and spatial database systems including IBM Informix Spatial and Geodetic DataBlades, ESRI SDE, Microsoft SQL server 2008, Oracle Spatial and PostGIS. In order to bridge this gap, we report our experience with implementing several optimization techniques in Oracle Spatial to speed up topological relationship query processing for query windows represented by complex regions (such as polygons or multi-polygons). Our experiments, utilizing real-world data sets, demonstrate that topological relationship query performance can be significantly improved using the proposed techniques.

Scalable and Reliable Location Services through Decentralized Replication

One of the critical challenges for service oriented computing systems is the capability to guarantee scalable and reliable service provision. This paper presents Reliable GeoGrid, a decentralized service computing architecture based on geographical location aware overlay network for supporting reliable and scalable mobile information delivery services. The reliable GeoGrid approach offers two distinct features. First, we develop a distributed replication scheme, aiming at providing scalable and reliable processing of location service requests in decentralized pervasive computing environments. Our replica management operates on a network of heterogeneous nodes and utilizes a shortcut-based optimization to increase the resilience of the system against node failures and network failures. Second, we devise a dynamic load balancing technique that exploits the service processing capabilities of replicas to scale the system in anticipation of unexpected workload changes and node failures by taking into account of node heterogeneity, network proximity, and changing workload at each node. Our experimental evaluation shows that the reliable GeoGrid architecture is highly scalable under changing service workloads with moving hotspots and highly reliable in the presence of both individual node failures and massive node failures.