The Experts below are selected from a list of 41952 Experts worldwide ranked by ideXlab platform
Mohamed F. Mokbel - One of the best experts on this subject based on the ideXlab platform.
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panda a predictive spatio temporal Query Processor
Advances in Geographic Information Systems, 2012Co-Authors: Abdeltawab M Hendawi, Mohamed F. MokbelAbstract:This paper presents the Panda system for efficient support of a wide variety of predictive spatio-temporal queries that are widely used in several applications including traffic management, location-based advertising, and ride sharing. Unlike previous attempts in supporting predictive queries, Panda targets long-term Query prediction as it relies on adapting a well-designed long-term prediction function to: (a) scale up to large number of moving objects, and (b) support large number of predictive queries. As a means of scalability, Panda smartly precomputes parts of the most frequent incoming predictive queries, which significantly reduces the Query response time. Panda employs a tunable threshold that achieves a trade-off between Query response time and the maintenance cost of precomptued answers. Experimental results, based on large data sets, show that Panda is scalable, efficient, and as accurate as its underlying prediction function.
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Casper*: Query processing for location services without compromising privacy
ACM Transactions on Database Systems, 2009Co-Authors: Chi-yin Chow, Mohamed F. Mokbel, Walid G. ArefAbstract:In this article, we present a new privacy-aware Query processing framework, Capser*, in which mobile and stationary users can obtain snapshot and/or continuous location-based services without revealing their private location information. In particular, we propose a privacy-aware Query Processor embedded inside a location-based database server to deal with snapshot and continuous queries based on the knowledge of the user's cloaked location rather than the exact location. Our proposed privacy-aware Query Processor is completely independent of how we compute the user's cloaked location. In other words, any existing location anonymization algorithms that blur the user's private location into cloaked rectilinear areas can be employed to protect the user's location privacy. We first propose a privacy-aware Query Processor that not only supports three new privacy-aware Query types, but also achieves a trade-off between Query processing cost and answer optimality. Then, to improve system scalability of processing continuous privacy-aware queries, we propose a shared execution paradigm that shares Query processing among a large number of continuous queries. The proposed scalable paradigm can be tuned through two parameters to trade off between system scalability and answer optimality. Experimental results show that our Query Processor achieves high quality snapshot and continuous location-based services while supporting queries and/or data with cloaked locations.
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the new casper Query processing for location services without compromising privacy
Very Large Data Bases, 2006Co-Authors: Mohamed F. Mokbel, Chi-yin Chow, Walid G. ArefAbstract:This paper tackles a major privacy concern in current location-based services where users have to continuously report their locations to the database server in order to obtain the service. For example, a user asking about the nearest gas station has to report her exact location. With untrusted servers, reporting the location information may lead to several privacy threats. In this paper, we present Casper1; a new framework in which mobile and stationary users can entertain location-based services without revealing their location information. Casper consists of two main components, the location anonymizer and the privacy-aware Query Processor. The location anonymizer blurs the users' exact location information into cloaked spatial regions based on user-specified privacy requirements. The privacy-aware Query Processor is embedded inside the location-based database server in order to deal with the cloaked spatial areas rather than the exact location information. Experimental results show that Casper achieves high quality location-based services while providing anonymity for both data and queries.
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Continuous Query Processing of Spatio-Temporal Data Streams in PLACE
GeoInformatica, 2005Co-Authors: Mohamed F. Mokbel, Xiaopeng Xiong, Moustafa A. Hammad, Walid G. ArefAbstract:The tremendous increase in the use of cellular phones, GPS-like devices, and RFIDs results in highly dynamic environments where objects as well as queries are continuously moving. In this paper, we present a continuous Query Processor designed specifically for highly dynamic environments (e.g., location-aware environments). We implemented the proposed continuous Query Processor inside the PLACE server (Pervasive Location-Aware Computing Environments); a scalable location-aware database server developed at Purdue University. The PLACE server extends data streaming management systems to support location-aware environments. These environments are characterized by the wide variety of continuous spatio-temporal queries and the unbounded spatio-temporal streams. The proposed continuous Query Processor includes: (1) New incremental spatio-temporal operators to support a wide variety of continuous spatio-temporal queries, (2) Extended semantics of sliding window queries to deal with spatial sliding windows as well as temporal sliding windows, and (3) A shared-execution framework for scalable execution of a set of concurrent continuous spatio-temporal queries. Experimental evaluation shows promising performance of the continuous Query Processor of the PLACE server.
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place a Query Processor for handling real time spatio temporal data streams
Very Large Data Bases, 2004Co-Authors: Mohamed F. Mokbel, Xiaopeng Xiong, Walid G. Aref, Susanne E Hambrusch, Sunil Prabhakar, Moustafa A. HammadAbstract:The emergence of location-aware services calls for new real-time spatio-temporal Query processing algorithms that deal with large numbers of mobile objects and queries. In this demo, we present PLACE (Pervasive Location-Aware Computing Environments); a scalable location-aware database server developed at Purdue University. The PLACE server addresses scalability by adopting an incremental evaluation mechanism for answering concurrently executing continuous spatio-temporal queries. The PLACE server supports a wide variety of stationery and moving continuous spatio-temporal queries through a set of pipelined spatio-temporal operators. The large numbers of moving objects generate real-time spatio-temporal data streams.
Moustafa A. Hammad - One of the best experts on this subject based on the ideXlab platform.
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Continuous Query Processing of Spatio-Temporal Data Streams in PLACE
GeoInformatica, 2005Co-Authors: Mohamed F. Mokbel, Xiaopeng Xiong, Moustafa A. Hammad, Walid G. ArefAbstract:The tremendous increase in the use of cellular phones, GPS-like devices, and RFIDs results in highly dynamic environments where objects as well as queries are continuously moving. In this paper, we present a continuous Query Processor designed specifically for highly dynamic environments (e.g., location-aware environments). We implemented the proposed continuous Query Processor inside the PLACE server (Pervasive Location-Aware Computing Environments); a scalable location-aware database server developed at Purdue University. The PLACE server extends data streaming management systems to support location-aware environments. These environments are characterized by the wide variety of continuous spatio-temporal queries and the unbounded spatio-temporal streams. The proposed continuous Query Processor includes: (1) New incremental spatio-temporal operators to support a wide variety of continuous spatio-temporal queries, (2) Extended semantics of sliding window queries to deal with spatial sliding windows as well as temporal sliding windows, and (3) A shared-execution framework for scalable execution of a set of concurrent continuous spatio-temporal queries. Experimental evaluation shows promising performance of the continuous Query Processor of the PLACE server.
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place a Query Processor for handling real time spatio temporal data streams
Very Large Data Bases, 2004Co-Authors: Mohamed F. Mokbel, Xiaopeng Xiong, Walid G. Aref, Susanne E Hambrusch, Sunil Prabhakar, Moustafa A. HammadAbstract:The emergence of location-aware services calls for new real-time spatio-temporal Query processing algorithms that deal with large numbers of mobile objects and queries. In this demo, we present PLACE (Pervasive Location-Aware Computing Environments); a scalable location-aware database server developed at Purdue University. The PLACE server addresses scalability by adopting an incremental evaluation mechanism for answering concurrently executing continuous spatio-temporal queries. The PLACE server supports a wide variety of stationery and moving continuous spatio-temporal queries through a set of pipelined spatio-temporal operators. The large numbers of moving objects generate real-time spatio-temporal data streams.
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nile a Query processing engine for data streams
International Conference on Data Engineering, 2004Co-Authors: Moustafa A. Hammad, Mohamed F. Mokbel, Walid G. Aref, Ann Christine Catlin, Ahmed K Elmagarmid, Mohamed Y Eltabakh, Mohamed G Elfeky, Thanaa M Ghanem, R Gwadera, Ihab F IlyasAbstract:We present the demonstration of the design of "STEAM", Purdue Boiler Makers' stream database system that allows for the processing of continuous and snap-shot queries over data streams. Specifically, the demonstration focuses on the Query processing engine, "Nile". Nile extends the Query Processor engine of an object-relational database management system, PREDATOR, to process continuous queries over data streams. Nile supports extended SQL operators that handle sliding-window execution as an approach to restrict the size of the stored state in operators such as join.
Walid G. Aref - One of the best experts on this subject based on the ideXlab platform.
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Casper*: Query processing for location services without compromising privacy
ACM Transactions on Database Systems, 2009Co-Authors: Chi-yin Chow, Mohamed F. Mokbel, Walid G. ArefAbstract:In this article, we present a new privacy-aware Query processing framework, Capser*, in which mobile and stationary users can obtain snapshot and/or continuous location-based services without revealing their private location information. In particular, we propose a privacy-aware Query Processor embedded inside a location-based database server to deal with snapshot and continuous queries based on the knowledge of the user's cloaked location rather than the exact location. Our proposed privacy-aware Query Processor is completely independent of how we compute the user's cloaked location. In other words, any existing location anonymization algorithms that blur the user's private location into cloaked rectilinear areas can be employed to protect the user's location privacy. We first propose a privacy-aware Query Processor that not only supports three new privacy-aware Query types, but also achieves a trade-off between Query processing cost and answer optimality. Then, to improve system scalability of processing continuous privacy-aware queries, we propose a shared execution paradigm that shares Query processing among a large number of continuous queries. The proposed scalable paradigm can be tuned through two parameters to trade off between system scalability and answer optimality. Experimental results show that our Query Processor achieves high quality snapshot and continuous location-based services while supporting queries and/or data with cloaked locations.
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the new casper Query processing for location services without compromising privacy
Very Large Data Bases, 2006Co-Authors: Mohamed F. Mokbel, Chi-yin Chow, Walid G. ArefAbstract:This paper tackles a major privacy concern in current location-based services where users have to continuously report their locations to the database server in order to obtain the service. For example, a user asking about the nearest gas station has to report her exact location. With untrusted servers, reporting the location information may lead to several privacy threats. In this paper, we present Casper1; a new framework in which mobile and stationary users can entertain location-based services without revealing their location information. Casper consists of two main components, the location anonymizer and the privacy-aware Query Processor. The location anonymizer blurs the users' exact location information into cloaked spatial regions based on user-specified privacy requirements. The privacy-aware Query Processor is embedded inside the location-based database server in order to deal with the cloaked spatial areas rather than the exact location information. Experimental results show that Casper achieves high quality location-based services while providing anonymity for both data and queries.
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Continuous Query Processing of Spatio-Temporal Data Streams in PLACE
GeoInformatica, 2005Co-Authors: Mohamed F. Mokbel, Xiaopeng Xiong, Moustafa A. Hammad, Walid G. ArefAbstract:The tremendous increase in the use of cellular phones, GPS-like devices, and RFIDs results in highly dynamic environments where objects as well as queries are continuously moving. In this paper, we present a continuous Query Processor designed specifically for highly dynamic environments (e.g., location-aware environments). We implemented the proposed continuous Query Processor inside the PLACE server (Pervasive Location-Aware Computing Environments); a scalable location-aware database server developed at Purdue University. The PLACE server extends data streaming management systems to support location-aware environments. These environments are characterized by the wide variety of continuous spatio-temporal queries and the unbounded spatio-temporal streams. The proposed continuous Query Processor includes: (1) New incremental spatio-temporal operators to support a wide variety of continuous spatio-temporal queries, (2) Extended semantics of sliding window queries to deal with spatial sliding windows as well as temporal sliding windows, and (3) A shared-execution framework for scalable execution of a set of concurrent continuous spatio-temporal queries. Experimental evaluation shows promising performance of the continuous Query Processor of the PLACE server.
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place a Query Processor for handling real time spatio temporal data streams
Very Large Data Bases, 2004Co-Authors: Mohamed F. Mokbel, Xiaopeng Xiong, Walid G. Aref, Susanne E Hambrusch, Sunil Prabhakar, Moustafa A. HammadAbstract:The emergence of location-aware services calls for new real-time spatio-temporal Query processing algorithms that deal with large numbers of mobile objects and queries. In this demo, we present PLACE (Pervasive Location-Aware Computing Environments); a scalable location-aware database server developed at Purdue University. The PLACE server addresses scalability by adopting an incremental evaluation mechanism for answering concurrently executing continuous spatio-temporal queries. The PLACE server supports a wide variety of stationery and moving continuous spatio-temporal queries through a set of pipelined spatio-temporal operators. The large numbers of moving objects generate real-time spatio-temporal data streams.
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nile a Query processing engine for data streams
International Conference on Data Engineering, 2004Co-Authors: Moustafa A. Hammad, Mohamed F. Mokbel, Walid G. Aref, Ann Christine Catlin, Ahmed K Elmagarmid, Mohamed Y Eltabakh, Mohamed G Elfeky, Thanaa M Ghanem, R Gwadera, Ihab F IlyasAbstract:We present the demonstration of the design of "STEAM", Purdue Boiler Makers' stream database system that allows for the processing of continuous and snap-shot queries over data streams. Specifically, the demonstration focuses on the Query processing engine, "Nile". Nile extends the Query Processor engine of an object-relational database management system, PREDATOR, to process continuous queries over data streams. Nile supports extended SQL operators that handle sliding-window execution as an approach to restrict the size of the stored state in operators such as join.
Andreas Harth - One of the best experts on this subject based on the ideXlab platform.
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shepherd a shipping based Query Processor to enhance sparql endpoint performance
International Semantic Web Conference, 2014Co-Authors: Maribel Acosta, Mariaesther Vidal, Fabian Flock, Simon Castillo, Carlos Builaranda, Andreas HarthAbstract:Recent studies reveal that publicly available SPARQL endpoints exhibit significant limitations in supporting real-world applications. In order for this Querying infrastructure to reach its full potential, more flexible client-server architectures capable of deciding appropriate shipping plans are needed. Shipping plans indicate how the execution of Query operators is distributed between the client and the server. We propose SHEPHERD, a SPARQL client-server Query Processor tailored to reduce SPARQL endpoint workload and generate shipping plans where costly operators are placed at the client site. We evaluated SHEPHERD on a variety of public SPARQL endpoints and SPARQL queries. Experimental results suggest that SHEPHERD can enhance endpoint performance while shifting workload from the endpoint to the client.
Joseph M Hellerstein - One of the best experts on this subject based on the ideXlab platform.
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declarative network monitoring with an underprovisioned Query Processor
International Conference on Data Engineering, 2006Co-Authors: Frederick Ralph Reiss, Joseph M HellersteinAbstract:Many of the data sources used in stream Query processing are known to exhibit bursty behavior. We focus here on passive network monitoring, an application in which the data rates typically exhibit a large peak-to-average ratio. Provisioning a stream Query Processor to handle peak rates in such a setting can be prohibitively expensive. In this paper, we propose to solve this problem by provisioning the Query Processor for typical data rates instead of much higher peak data rates. To enable this strategy, we present mechanisms and policies for managing the tradeoffs between the latency and accuracy of Query results when bursts exceed the steady-state capacity of the Query Processor. We describe the current status of our implementation and present experimental results on a testbed network monitoring application to demonstrate the utility of our approach
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ICDE - Declarative Network Monitoring with an Underprovisioned Query Processor
22nd International Conference on Data Engineering (ICDE'06), 2006Co-Authors: Frederick Ralph Reiss, Joseph M HellersteinAbstract:Many of the data sources used in stream Query processing are known to exhibit bursty behavior. We focus here on passive network monitoring, an application in which the data rates typically exhibit a large peak-to-average ratio. Provisioning a stream Query Processor to handle peak rates in such a setting can be prohibitively expensive. In this paper, we propose to solve this problem by provisioning the Query Processor for typical data rates instead of much higher peak data rates. To enable this strategy, we present mechanisms and policies for managing the tradeoffs between the latency and accuracy of Query results when bursts exceed the steady-state capacity of the Query Processor. We describe the current status of our implementation and present experimental results on a testbed network monitoring application to demonstrate the utility of our approach
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declarative network monitoring with an underprovisioned Query Processor extended version
2006Co-Authors: Frederick Ralph Reiss, Joseph M HellersteinAbstract:Many of the data sources used in stream Query processing are known to exhibit bursty behavior. We focus here on passive network monitoring, an application in which the data rates typically exhibit a large peak-to-average ratio. Provisioning a stream Query Processor to handle peak rates in such a setting can be prohibitively expensive. In this paper, we propose to solve this problem by provisioning the Query Processor for typical data rates instead of much higher peak data rates. To enable this strategy, we present mechanisms and policies for managing the tradeoffs between the latency and accuracy of Query results when bursts exceed the steady-state capacity of the Query Processor. We describe the current status of our implementation and present experimental results on a testbed network monitoring application to demonstrate the utility of our approach.
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enhancing p2p file sharing with an internet scale Query Processor
Very Large Data Bases, 2004Co-Authors: Boon Thau Loo, Joseph M Hellerstein, Ryan Huebsch, Scott Shenker, Ion StoicaAbstract:In this paper, we address the problem of designing a scalable, accurate Query Processor for peer-to-peer filesharing and similar distributed keyword search systems. Using a globally-distributed monitoring infrastructure, we perform an extensive study of the Gnutella filesharing network, characterizing its topology, data and Query workloads. We observe that Gnutella's Query processing approach performs well for popular content, but quite poorly for rare items with few replicas. We then consider an alternate approach based on Distributed Hash Tables (DHTs). We describe our implementation of PIERSearch, a DHT-based system, and propose a hybrid system where Gnutella is used to locate popular items, and PIERSearch for handling rare items. We develop an analytical model of the two approaches, and use it in concert with our Gnutella traces to study the trade-off between Query recall and system overhead of the hybrid system. We evaluate a variety of localized schemes for identifying items that are rare and worth handling via the DHT. Lastly, we show in a live deployment on fifty nodes on two continents that it nicely complements Gnutella in its ability to handle rare items.
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the design of an acquisitional Query Processor for sensor networks
International Conference on Management of Data, 2003Co-Authors: Samuel Madden, Joseph M Hellerstein, Michael J Franklin, Wei HongAbstract:We discuss the design of an acquisitional Query Processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to Query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence Query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed Query Processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices.
