Convergence of Array DBMS and Cellular Automata: A Road Traffic Simulation Case
Proceedings of ISP RAS are a double-blind peer-reviewed journal publishing scientific articles in the areas of system programming, software engineering, and computer science. The journal's goal is to develop a respected network of knowledge in the mentioned above areas by publishing high quality articles on open access. The journal is intended for researchers, students, and practitioners.
Immense volumes of geospatial arrays are generated daily. Examples of such include satellite imagery, numerical simulation, and derivative dataavalanche. Array DBMS are one of the prominent tools for working with large geospatial arrays. Usually the arrays natively come as raster files. ChronosDB is a novel distributed, file based, geospatial array DBMS: chronosdb.gis.land . ChronosDB operates directly on raster files, delegates array processing to existing elaborate command line tools, and outperforms SciDB by up to 75 × on average. This demonstration will showcase three new components of ChronosDB enabling users to interact with the system and appreciate its benefits: (i) a WebGUI (edit, submit queries and get the output), (ii) an execution plan explainer (investigate the generated DAG), and (iii) a dataset visualizer (display ChronosDB arrays on an interactive web map).
Earth remote sensing imagery come from satellites, unmanned aerial vehicles, airplanes, and other sources. National agencies, commercial companies, and individuals across the globe collect enormous amounts of such imagery daily. Array DBMS are one of the prominent tools to manage and process large volumes of geospatial imagery. Recently we presented ChronosDB — innovative geospatial array DBMS that outperforms SciDB by up to 75× on average. SciDB is the only freely available distributed array DBMS to date. Unlike SciDB, ChronosDB does not require importing files into an internal DBMS format and works with imagery “in situ”: directly in their native file formats. This is one of the many virtues of ChronosDB. In this paper, we investigate the impact of data compression on the performance of array processing operations. We compress the data with diverse methods and explore compression impact on the processing speed. We thoroughly compare the performance on source and compressed data in ChronosDB and SciDB on real-world data on computer clusters in Microsoft Azure Cloud.
This paper presents how machine learning algorithms and methods of statistics can be implemented to data management in hybrid data storage systems. Basicly, two di↵erent storage types are used to store data in the hybrid data storage systems. Keeping low-frequenty used data on cheap and slow storages of type one and high-frequently used data on fast and expensive storages of type two helps to achieve optimal performance/cost ratio for the system. We use classification algorithms to estimate probability that the data will high-frequently used in future. Then, using the risks analysis we define where the data should be stored. We show how to estimate optimal number of replicas of the data using regression algorithms and Hidden Markov Model. Based on the probability, risks and the optimal nuber of data replicas our recommendation system finds optimal data distribution in the hybrid data storage system. We present the results of our method implementation in LHCb hybrid data storage.
Modern passenger terminals are characterized by dynamic processes variability, diverse options consideration, taking into account the criteria of safety, reliability analysis and the continuous research of passenger processing. For any modern marine passenger terminal it is necessary to use the tool to simulate passenger flows. In this way it is possible to obtain the analytical information and use it for decision-making when solving the problem of the amount of personnel required for passenger services.in line with the original ship arrival schedule, to solve problems of forecasting groups at the terminal. Of particular relevance is the choice of the mathematical transport model and the practical conditions for the implementation of the model in the real terminal operation. In this article the analysis technique of simulation-based terminal services, provides a mathematical model of passenger movement inside the terminal. Also, the conditions of implementation of the transportation model during the operation of marine passenger terminal are examined. The object of the research is the marine Passenger Port of St. Petersburg “Marine Facade”. The paper discusses advantages of using such systems and their introduction in the early stages of operation of the terminal. In addition, the conclusion about the effectiveness of such systems for the analysis of the correctness of internal space of the marine terminal. The study represents an example of analytical information used for the forecast of the terminal operations, the analysis of the workload and efficiency of the organization of the marine terminal.
Modern processes in the world economy directly affect the development and changes in sea passenger ports and their infrastructure. The principles of organization of the “city-sea passenger port” system are changing and becoming more complex. Recently there has been a significant increase in passenger traffic and cruise ship and ferry traffic in Baltic Sea. Since these objects are complex technical systems consisting of many elements, in their study it is necessary to use the system approach, to solve the problem of structure synthesis and the determination of objective functions. The objective of this publication was to study how the forecast for the development of demand for sea passenger ports (number cruise ships and passengers flow for next year) could be done by combining simulation and forecast functions. These tasks depend on the qualitative construction of specialized information simulation models. Such subsystems should be used by passenger port management for both operational everyday tasks and strategic tasks. One of the main goals of the forecast is the qualitative construction of an analytical function work of the terminal that determines the passenger flow based on real data. The article considers the solution of this problem by using the method of average growth rate and polynomial extrapolation. In the article, the characteristics and infrastructural features of the passenger ports of St. Petersburg are given, and the main directions of development based on the results of simulation are considered. The paper discusses advantages of using such forecast and their introduction in the early stages of operation of the terminal. The study represents an example of analytical information used for the forecast of the terminal load, the analysis of the workload and efficiency of the organization of the marine terminal in operational tasks using analytical function based on real data