The Experts below are selected from a list of 675 Experts worldwide ranked by ideXlab platform
Timo Koskela - One of the best experts on this subject based on the ideXlab platform.
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A distributed POI Data model based on the entity-component approach
2014 IEEE Symposium on Computers and Communications (ISCC), 2014Co-Authors: Arto Heikkinen, Ari Okkonen, Antti Karhu, Timo KoskelaAbstract:Point of interest (POI) information is commonly utilized in different location-based services, which are becoming more popular due to widespread adoption of GPS-enabled smartphones. This paper presents a distributed and modular Data model for representing POIs. In addition, RESTful service architecture for accessing the POI Data is described. The design of the POI Data model is based on the principles of the entity-component model, which allows partitioning the POI Data into separate components that are linked to a POI entity. The performance of the POI Data model was evaluated in terms of Data volume, client-side Data Parsing time and client-side memory consumption using a mobile web application. The POI Data model was also compared with an existing POI implementation. Based on the measurements, the ability to select only relevant Data components was found very important feature for achieving good client-side performance.
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ISCC - A distributed POI Data model based on the entity-component approach
2014 IEEE Symposium on Computers and Communications (ISCC), 2014Co-Authors: Arto Heikkinen, Ari Okkonen, Antti Karhu, Timo KoskelaAbstract:Point of interest (POI) information is commonly utilized in different location-based services, which are becoming more popular due to widespread adoption of GPS-enabled smartphones. This paper presents a distributed and modular Data model for representing POIs. In addition, RESTful service architecture for accessing the POI Data is described. The design of the POI Data model is based on the principles of the entity-component model, which allows partitioning the POI Data into separate components that are linked to a POI entity. The performance of the POI Data model was evaluated in terms of Data volume, client-side Data Parsing time and client-side memory consumption using a mobile web application. The POI Data model was also compared with an existing POI implementation. Based on the measurements, the ability to select only relevant Data components was found very important feature for achieving good client-side performance.
G. E. Tita - One of the best experts on this subject based on the ideXlab platform.
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Measuring and Modeling Repeat and Near-Repeat Burglary Effects
Journal of Quantitative Criminology, 2009Co-Authors: M. B. Short, M. R. D’orsogna, P. J. Brantingham, G. E. TitaAbstract:We develop a mathematical framework aimed at analyzing repeat and near-repeat effects in crime Data. Parsing burglary Data from Long Beach, CA according to different counting methods, we determine the probability distribution functions for the time interval τ between repeat offenses. We then compare these observed distributions to theoretically derived distributions in which the repeat effects are due solely to persistent risk heterogeneity. We find that risk heterogeneity alone cannot explain the observed distributions, while a form of event dependence (boosts) can. Using this information, we model repeat victimization as a series of random events, the likelihood of which changes each time an offense occurs. We are able to estimate typical time scales for repeat burglary events in Long Beach by fitting our Data to this model. Computer simulations of this model using these observed parameters agree with the empirical Data.
Arto Heikkinen - One of the best experts on this subject based on the ideXlab platform.
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A distributed POI Data model based on the entity-component approach
2014 IEEE Symposium on Computers and Communications (ISCC), 2014Co-Authors: Arto Heikkinen, Ari Okkonen, Antti Karhu, Timo KoskelaAbstract:Point of interest (POI) information is commonly utilized in different location-based services, which are becoming more popular due to widespread adoption of GPS-enabled smartphones. This paper presents a distributed and modular Data model for representing POIs. In addition, RESTful service architecture for accessing the POI Data is described. The design of the POI Data model is based on the principles of the entity-component model, which allows partitioning the POI Data into separate components that are linked to a POI entity. The performance of the POI Data model was evaluated in terms of Data volume, client-side Data Parsing time and client-side memory consumption using a mobile web application. The POI Data model was also compared with an existing POI implementation. Based on the measurements, the ability to select only relevant Data components was found very important feature for achieving good client-side performance.
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ISCC - A distributed POI Data model based on the entity-component approach
2014 IEEE Symposium on Computers and Communications (ISCC), 2014Co-Authors: Arto Heikkinen, Ari Okkonen, Antti Karhu, Timo KoskelaAbstract:Point of interest (POI) information is commonly utilized in different location-based services, which are becoming more popular due to widespread adoption of GPS-enabled smartphones. This paper presents a distributed and modular Data model for representing POIs. In addition, RESTful service architecture for accessing the POI Data is described. The design of the POI Data model is based on the principles of the entity-component model, which allows partitioning the POI Data into separate components that are linked to a POI entity. The performance of the POI Data model was evaluated in terms of Data volume, client-side Data Parsing time and client-side memory consumption using a mobile web application. The POI Data model was also compared with an existing POI implementation. Based on the measurements, the ability to select only relevant Data components was found very important feature for achieving good client-side performance.
Timothy Sakharov - One of the best experts on this subject based on the ideXlab platform.
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KDIR - Data Parsing using tier grammars
Proceedings of the 7th International Joint Conference on Knowledge Discovery Knowledge Engineering and Knowledge Management, 2015Co-Authors: Alexander Sakharov, Timothy SakharovAbstract:Parsing turns unstructured Data into structured Data suitable for knowledge discovery and querying. The complexity of grammar notations and the difficulty of grammar debugging limit the availability of Data parsers. Tier grammars are defined by simply dividing terminals into predefined classes and then splitting elements of some classes into multiple layered sub-groups. The set of predefined terminal classes can be easily extended. Tier grammars and their extensions are LL(1) grammars. Tier grammars are a tool for big Data preprocessing.
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Data Parsing using tier grammars
2015 7th International Joint Conference on Knowledge Discovery Knowledge Engineering and Knowledge Management (IC3K), 2015Co-Authors: Alexander Sakharov, Timothy SakharovAbstract:Parsing turns unstructured Data into structured Data suitable for knowledge discovery and querying. The complexity of grammar notations and the difficulty of grammar debugging limit the availability of Data parsers. Tier grammars are defined by simply dividing terminals into predefined classes and then splitting elements of some classes into multiple layered sub-groups. The set of predefined terminal classes can be easily extended. Tier grammars and their extensions are LL(1) grammars. Tier grammars are a tool for big Data preprocessing.
Jun Wang - One of the best experts on this subject based on the ideXlab platform.
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An integrated system for building structural health monitoring and early warning based on an Internet of things approach
International Journal of Distributed Sensor Networks, 2017Co-Authors: Jun Wang, Yongfeng Fu, Xiaokang YangAbstract:The intelligent security monitoring of buildings and their surroundings has become increasingly crucial as the number of high-rise buildings increases. Building structural health monitoring and early warning technology are key components of building safety, the implementation of which remains challenging, and the Internet of things approach provides a new technical measure for addressing this challenge. This article presents a novel integrated information system that combines Internet of things, building information management, early warning system, and cloud services. Specifically, the system involves an intelligent Data box with enhanced connectivity and exchangeability for accessing and integrating the Data obtained from distributed heterogeneous sensing devices. An extensible markup language (XML)–based uniform Data Parsing model is proposed to abstract the various message formats of heterogeneous devices to ensure Data integration. The proposed Internet of things–based integrated information system s...
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JCDL - Research networks in Data repositories
IEEE ACM Joint Conference on Digital Libraries, 2014Co-Authors: Mark R. Costa, Jian Qin, Jun WangAbstract:This paper reports our ongoing work investigating the structural features of scientific collaboration based on metaData collected from a scientific Data repository (SDR). The background literature is reviewed in supporting our claim that metaData collected from SDRs offer a complimentary Data source to traditional publication metaData collected from digital libraries. Methodological considerations are discussed in association with using metaData from SDRs, including author name disambiguation and Data Parsing. Initial findings show that the network has some unique macro-level structural features while also in agreement with existing networks theories. Challenges due to inconsistent metaData quality control procedures are also discussed in an attempt to reinforce claims that metaData should be designed to support both domain specific retrieval and evaluation and assessment needs.
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Research networks in Data repositories
IEEE ACM Joint Conference on Digital Libraries, 2014Co-Authors: Mark R. Costa, Jian Qin, Jun WangAbstract:This paper reports our ongoing work investigating the structural features of scientific collaboration based on metaData collected from a scientific Data repository (SDR). The background literature is reviewed in supporting our claim that metaData collected from SDRs offer a complimentary Data source to traditional publication metaData collected from digital libraries. Methodological considerations are discussed in association with using metaData from SDRs, including author name disambiguation and Data Parsing. Initial findings show that the network has some unique macro-level structural features while also in agreement with existing networks theories. Challenges due to inconsistent metaData quality control procedures are also discussed in an attempt to reinforce claims that metaData should be designed to support both domain specific retrieval and evaluation and assessment needs.
