The Experts below are selected from a list of 44325 Experts worldwide ranked by ideXlab platform
Bartosz Wilk - One of the best experts on this subject based on the ideXlab platform.
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ICCS - Towards an operational Database for real-time environmental monitoring and early warning systems
Procedia Computer Science, 2017Co-Authors: Bartosz Balis, Marian Bubak, Daniel Harezlak, Piotr Nowakowski, Maciej Pawlik, Bartosz WilkAbstract:Abstract Real-time environmental monitoring, early warning and decision support systems (EMEWD) require advanced management of operational data, i.e. recent sensor data needed for the assessment of the current situation. In this paper we evaluate the suitability of four data models and corresponding Database technologies – MongoDB document Database, PostgreSQL relational Database, Redis dictionary data server and InfluxDB time series Database – to serve as an operational Database for EMEWD systems. For each of the evaluated Databases, we design alternative data models to represent time series data, and experimentally evaluate each of them. We also perform comparative performance evaluation of all Databases, using the best model in each case. We have designed performance tests which reflect realistic conditions, using mixed workloads (simultaneous read and write operations) and queries typical for a smart levee monitoring and flood decision support system. Overall the results of the experiments allow us to answer interesting questions, such as: (1) how best to implement time series in a given data model? (2) What are the reasonable operational Database Volume limits? (3) What are the performance limits for different types of Databases?
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towards an operational Database for real time environmental monitoring and early warning systems
International Conference on Conceptual Structures, 2017Co-Authors: Bartosz Balis, Marian Bubak, Daniel Harezlak, Piotr Nowakowski, Maciej Pawlik, Bartosz WilkAbstract:Abstract Real-time environmental monitoring, early warning and decision support systems (EMEWD) require advanced management of operational data, i.e. recent sensor data needed for the assessment of the current situation. In this paper we evaluate the suitability of four data models and corresponding Database technologies – MongoDB document Database, PostgreSQL relational Database, Redis dictionary data server and InfluxDB time series Database – to serve as an operational Database for EMEWD systems. For each of the evaluated Databases, we design alternative data models to represent time series data, and experimentally evaluate each of them. We also perform comparative performance evaluation of all Databases, using the best model in each case. We have designed performance tests which reflect realistic conditions, using mixed workloads (simultaneous read and write operations) and queries typical for a smart levee monitoring and flood decision support system. Overall the results of the experiments allow us to answer interesting questions, such as: (1) how best to implement time series in a given data model? (2) What are the reasonable operational Database Volume limits? (3) What are the performance limits for different types of Databases?
Bartosz Balis - One of the best experts on this subject based on the ideXlab platform.
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ICCS - Towards an operational Database for real-time environmental monitoring and early warning systems
Procedia Computer Science, 2017Co-Authors: Bartosz Balis, Marian Bubak, Daniel Harezlak, Piotr Nowakowski, Maciej Pawlik, Bartosz WilkAbstract:Abstract Real-time environmental monitoring, early warning and decision support systems (EMEWD) require advanced management of operational data, i.e. recent sensor data needed for the assessment of the current situation. In this paper we evaluate the suitability of four data models and corresponding Database technologies – MongoDB document Database, PostgreSQL relational Database, Redis dictionary data server and InfluxDB time series Database – to serve as an operational Database for EMEWD systems. For each of the evaluated Databases, we design alternative data models to represent time series data, and experimentally evaluate each of them. We also perform comparative performance evaluation of all Databases, using the best model in each case. We have designed performance tests which reflect realistic conditions, using mixed workloads (simultaneous read and write operations) and queries typical for a smart levee monitoring and flood decision support system. Overall the results of the experiments allow us to answer interesting questions, such as: (1) how best to implement time series in a given data model? (2) What are the reasonable operational Database Volume limits? (3) What are the performance limits for different types of Databases?
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towards an operational Database for real time environmental monitoring and early warning systems
International Conference on Conceptual Structures, 2017Co-Authors: Bartosz Balis, Marian Bubak, Daniel Harezlak, Piotr Nowakowski, Maciej Pawlik, Bartosz WilkAbstract:Abstract Real-time environmental monitoring, early warning and decision support systems (EMEWD) require advanced management of operational data, i.e. recent sensor data needed for the assessment of the current situation. In this paper we evaluate the suitability of four data models and corresponding Database technologies – MongoDB document Database, PostgreSQL relational Database, Redis dictionary data server and InfluxDB time series Database – to serve as an operational Database for EMEWD systems. For each of the evaluated Databases, we design alternative data models to represent time series data, and experimentally evaluate each of them. We also perform comparative performance evaluation of all Databases, using the best model in each case. We have designed performance tests which reflect realistic conditions, using mixed workloads (simultaneous read and write operations) and queries typical for a smart levee monitoring and flood decision support system. Overall the results of the experiments allow us to answer interesting questions, such as: (1) how best to implement time series in a given data model? (2) What are the reasonable operational Database Volume limits? (3) What are the performance limits for different types of Databases?
W. Herrmann - One of the best experts on this subject based on the ideXlab platform.
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(BARS) -- Bibliographic Retrieval System Sandia shock compression (SSC) Database shock physics index (SPHINX) Database. Volume 3, UNIX version Systems Guide
1993Co-Authors: G.m. Von Laven, W. HerrmannAbstract:The Bibliographic Retrieval System (BARS) is a Database management system specially designed to store and retrieve bibliographic references and track documents. The system uses INGRES to manage this Database and user interface. It uses forms for journal articles, books, conference proceedings, theses, technical reports, letters, memos, visual aids, as well as a miscellaneous form which can be used for data sets or any other material which can be assigned an access or file number. Sorted output resulting from flexible BOOLEAN searches can be printed or saved in files which can be inserted in reference lists for use with word processors.
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(BARS) -- Bibliographic Retrieval System Sandia Shock Compression (SSC) Database Shock Physics Index (SPHINX) Database. Volume 1: UNIX version query guide customized application for INGRES
1993Co-Authors: W. Herrmann, G.m. Von Laven, T. ParkerAbstract:The Bibliographic Retrieval System (BARS) is a data base management system specially designed to retrieve bibliographic references. Two Databases are available, (i) the Sandia Shock Compression (SSC) Database which contains over 5700 references to the literature related to stress waves in solids and their applications, and (ii) the Shock Physics Index (SPHINX) which includes over 8000 further references to stress waves in solids, material properties at intermediate and low rates, ballistic and hypervelocity impact, and explosive or shock fabrication methods. There is some overlap in the information in the two data bases.
Daniel Harezlak - One of the best experts on this subject based on the ideXlab platform.
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ICCS - Towards an operational Database for real-time environmental monitoring and early warning systems
Procedia Computer Science, 2017Co-Authors: Bartosz Balis, Marian Bubak, Daniel Harezlak, Piotr Nowakowski, Maciej Pawlik, Bartosz WilkAbstract:Abstract Real-time environmental monitoring, early warning and decision support systems (EMEWD) require advanced management of operational data, i.e. recent sensor data needed for the assessment of the current situation. In this paper we evaluate the suitability of four data models and corresponding Database technologies – MongoDB document Database, PostgreSQL relational Database, Redis dictionary data server and InfluxDB time series Database – to serve as an operational Database for EMEWD systems. For each of the evaluated Databases, we design alternative data models to represent time series data, and experimentally evaluate each of them. We also perform comparative performance evaluation of all Databases, using the best model in each case. We have designed performance tests which reflect realistic conditions, using mixed workloads (simultaneous read and write operations) and queries typical for a smart levee monitoring and flood decision support system. Overall the results of the experiments allow us to answer interesting questions, such as: (1) how best to implement time series in a given data model? (2) What are the reasonable operational Database Volume limits? (3) What are the performance limits for different types of Databases?
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towards an operational Database for real time environmental monitoring and early warning systems
International Conference on Conceptual Structures, 2017Co-Authors: Bartosz Balis, Marian Bubak, Daniel Harezlak, Piotr Nowakowski, Maciej Pawlik, Bartosz WilkAbstract:Abstract Real-time environmental monitoring, early warning and decision support systems (EMEWD) require advanced management of operational data, i.e. recent sensor data needed for the assessment of the current situation. In this paper we evaluate the suitability of four data models and corresponding Database technologies – MongoDB document Database, PostgreSQL relational Database, Redis dictionary data server and InfluxDB time series Database – to serve as an operational Database for EMEWD systems. For each of the evaluated Databases, we design alternative data models to represent time series data, and experimentally evaluate each of them. We also perform comparative performance evaluation of all Databases, using the best model in each case. We have designed performance tests which reflect realistic conditions, using mixed workloads (simultaneous read and write operations) and queries typical for a smart levee monitoring and flood decision support system. Overall the results of the experiments allow us to answer interesting questions, such as: (1) how best to implement time series in a given data model? (2) What are the reasonable operational Database Volume limits? (3) What are the performance limits for different types of Databases?
Piotr Nowakowski - One of the best experts on this subject based on the ideXlab platform.
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ICCS - Towards an operational Database for real-time environmental monitoring and early warning systems
Procedia Computer Science, 2017Co-Authors: Bartosz Balis, Marian Bubak, Daniel Harezlak, Piotr Nowakowski, Maciej Pawlik, Bartosz WilkAbstract:Abstract Real-time environmental monitoring, early warning and decision support systems (EMEWD) require advanced management of operational data, i.e. recent sensor data needed for the assessment of the current situation. In this paper we evaluate the suitability of four data models and corresponding Database technologies – MongoDB document Database, PostgreSQL relational Database, Redis dictionary data server and InfluxDB time series Database – to serve as an operational Database for EMEWD systems. For each of the evaluated Databases, we design alternative data models to represent time series data, and experimentally evaluate each of them. We also perform comparative performance evaluation of all Databases, using the best model in each case. We have designed performance tests which reflect realistic conditions, using mixed workloads (simultaneous read and write operations) and queries typical for a smart levee monitoring and flood decision support system. Overall the results of the experiments allow us to answer interesting questions, such as: (1) how best to implement time series in a given data model? (2) What are the reasonable operational Database Volume limits? (3) What are the performance limits for different types of Databases?
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towards an operational Database for real time environmental monitoring and early warning systems
International Conference on Conceptual Structures, 2017Co-Authors: Bartosz Balis, Marian Bubak, Daniel Harezlak, Piotr Nowakowski, Maciej Pawlik, Bartosz WilkAbstract:Abstract Real-time environmental monitoring, early warning and decision support systems (EMEWD) require advanced management of operational data, i.e. recent sensor data needed for the assessment of the current situation. In this paper we evaluate the suitability of four data models and corresponding Database technologies – MongoDB document Database, PostgreSQL relational Database, Redis dictionary data server and InfluxDB time series Database – to serve as an operational Database for EMEWD systems. For each of the evaluated Databases, we design alternative data models to represent time series data, and experimentally evaluate each of them. We also perform comparative performance evaluation of all Databases, using the best model in each case. We have designed performance tests which reflect realistic conditions, using mixed workloads (simultaneous read and write operations) and queries typical for a smart levee monitoring and flood decision support system. Overall the results of the experiments allow us to answer interesting questions, such as: (1) how best to implement time series in a given data model? (2) What are the reasonable operational Database Volume limits? (3) What are the performance limits for different types of Databases?
