Multiple-Aspect Analysis of Semantic Trajectories
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Spatiotemporal Data Mining: Issues, Tasks And
International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.1, February 2012 SPATIOTEMPORAL DATA MINING : ISSUES , TASKS AND APPLICATIONS K.Venkateswara Rao 1, A.Govardhan 2 and K.V.Chalapati Rao 1 1Department of Computer Science and Engineering, CVR College of Engineering, Ibrahimpatnam RR District, Andhra Pradesh, India [email protected] [email protected] 2JNTUH, Hyderabad, Andhra Pradesh, India [email protected] ABSTRACT Spatiotemporal data usually contain the states of an object, an event or a position in space over a period of time. Vast amount of spatiotemporal data can be found in several application fields such as traffic management, environment monitoring, and weather forecast. These datasets might be collected at different locations at various points of time in different formats. It poses many challenges in representing, processing, analysis and mining of such datasets due to complex structure of spatiotemporal objects and the relationships among them in both spatial and temporal dimensions. In this paper, the issues and challenges related to spatiotemporal data representation, analysis, mining and visualization of knowledge are presented. Various kinds of data mining tasks such as association rules, classification clustering for discovering knowledge from spatiotemporal datasets are examined and reviewed. System functional requirements for such kind of knowledge discovery and database structure are discussed. Finally applications of spatiotemporal data mining are presented. KEYWORDS Spatiotemporal data mining, spatiotemporal data mining issues, spatiotemporal data mining tasks, spatiotemporal data mining applications 1. INTRODUCTION A spatiotemporal object can be defined as an object that has at least one spatial and one temporal property. The spatial properties are location and geometry of the object. -
A Workbench for Advanced Database Implementation and Benchmarking Valliappan Narayanan Iowa State University
Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2009 A workbench for advanced database implementation and benchmarking Valliappan Narayanan Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Computer Sciences Commons Recommended Citation Narayanan, Valliappan, "A workbench for advanced database implementation and benchmarking" (2009). Graduate Theses and Dissertations. 10667. https://lib.dr.iastate.edu/etd/10667 This Thesis is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. A workbench for advanced database implementation and benchmarking by Valliappan Narayanan A thesis submitted to the graduate faculty in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Major: Computer Science Program of Study Committee: Shashi K. Gadia, Major Professor Johnny S. Wong Arun K. Somani Iowa State University Ames, Iowa 2009 Copyright © Valliappan Narayanan, 2009. All rights reserved. ii TABLE OF CONTENTS LIST OF FIGURES . vi ABSTRACT . vii CHAPTER 1. INTRODUCTION . 1 1.1. XML in our implementation . 1 1.2. Introduction to CanstoreX . 3 1.3. Query Engine . 4 1.4. Parametric Data Model. 5 1.4.1. TempDB: a prototype for a temporal database . 5 1.4.2. NC-94: A prototype for spatiotemporal database . 6 1.5. Files, command-based paradigm, and a common GUI . 6 1.6. Contributions under the current project . -
Graph Databases – Are They Really So New
International Journal of Advances in Science Engineering and Technology, ISSN: 2321-9009 Volume- 4, Issue-4, Oct.-2016 GRAPH DATABASES – ARE THEY REALLY SO NEW 1MIRKO MALEKOVIC, 2KORNELIJE RABUZIN, 3MARTINA SESTAK 1,2,3Faculty of Organization and Informatics Varaždin, University of Zagreb, Croatia E-mail: [email protected], [email protected], [email protected] Abstract- Even though relational databases have been (and still are) the most widely used database solutions for many years, there were other database solutions in use before the era of relational databases. One of those solutions were network databases with the underlying network model, whose characteristics will be presented in detail in this paper. The network model will be compared to the graph data model used by graph databases, the relatively new category of NoSQL databases with a growing share in the database market. The similarities and differences will be shown through the implementation of a simple database using network and graph data model. Keywords- Network data model, graph data model, nodes, relationships, records, sets. I. INTRODUCTION ● Hierarchical databases This type of databases is based on the hierarchical The relational databases were introduced by E. G. data model, whose structure can be represented as a Codd in his research papers in 1970s. In those papers layered tree in which one data table represents the he discussed relational model as the underlying data root of that tree (top layer), and the others represent model for the relational databases, which was based tree branches (nodes) emerging from the root of the on relational algebra and first-order predicate logic. -
Spatiotemporal Load-Analysis Model for Electric Power Distribution Facilities Using Consumer Meter-Reading Data
736 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 26, NO. 2, APRIL 2011 Spatiotemporal Load-Analysis Model for Electric Power Distribution Facilities Using Consumer Meter-Reading Data Jin-Ho Shin, Bong-Jae Yi, Young-Il Kim, Heon-Gyu Lee, and Keun Ho Ryu, Member, IEEE Abstract—The load analysis for the distribution system and the power market requires exploring more progressive and ef- facilities has relied on measurement equipment. Moreover, load ficient system operation to expand social welfare and to reduce monitoring incurs huge costs in terms of installation and main- the electricity bill. tenance. This paper presents a new model to analyze wherein facilities load under a feeder every 15 min using meter-reading Even though massive investments are made in new distribu- data that can be obtained from a power consumer every 15 min or tion lines and in the operation of equipment, an imbalance in a month even without setting up any measuring equipment. After power-supply equipment, such as power outages due to exces- the data warehouse is constructed by interfacing the legacy system sive investment or a lack of investment, low-voltage areas, and required for the load calculation, the relationship between the unused power equipment are predicted depending on the area. distribution system and the power consumer is established. Once the load pattern is forecasted by applying a clustering and clas- This problem stems from the considerable difficulty in checking sification algorithm of temporal data-mining techniques for the the load which changes every moment, checking the section power customer who is not involved in automatic meter reading, with the maximum load of the distribution line, and checking a single-line diagram per feeder is created, and power-flow cal- the equipment/load information of the individual transformer. -
The Role of Spatio-Temporal Information to Govern the COVID-19 Pandemic: a European Perspective
International Journal of Geo-Information Article The Role of Spatio-Temporal Information to Govern the COVID-19 Pandemic: A European Perspective Hartmut Müller 1,* and Marije Louwsma 2 1 School of Technology—Geoinformatics and Surveying, Mainz University of Applied Sciences, D-55118 Mainz, Germany 2 Cadastre, Land Registry and Mapping Agency, 7300 GH Apeldoorn, The Netherlands; [email protected] * Correspondence: [email protected] Abstract: The Covid-19 pandemic put a heavy burden on member states in the European Union. To govern the pandemic, having access to reliable geo-information is key for monitoring the spatial distribution of the outbreak over time. This study aims to analyze the role of spatio-temporal information in governing the pandemic in the European Union and its member states. The European Nomenclature of Territorial Units for Statistics (NUTS) system and selected national dashboards from member states were assessed to analyze which spatio-temporal information was used, how the information was visualized and whether this changed over the course of the pandemic. Initially, member states focused on their own jurisdiction by creating national dashboards to monitor the pandemic. Information between member states was not aligned. Producing reliable data and timeliness reporting was problematic, just like selecting indictors to monitor the spatial distribution and intensity of the outbreak. Over the course of the pandemic, with more knowledge about the virus and its characteristics, interventions of member states to govern the outbreak were better aligned Citation: Müller, H.; Louwsma, M. at the European level. However, further integration and alignment of public health data, statistical The Role of Spatio-Temporal Information to Govern the COVID-19 data and spatio-temporal data could provide even better information for governments and actors Pandemic: A European Perspective. -
Appendix E: an Overview of the Network Data Model
Elmasri_APPC Page 1 Monday, April 3, 2006 3:40 PM An Overview of the APPENDIXE Network Data Model This appendix provides an overview of the network data model.1 The original network model and language were presented in the CODASYL Data Base Task Group’s 1971 report; hence it is sometimes called the DBTG model. Revised reports in 1978 and 1981 incorporated more recent concepts. In this appendix, rather than concentrating on the details of a particular CODASYL report, we present the general concepts behind net- work-type databases and use the term network model rather than CODASYL model or DBTG model. The original CODASYL/DBTG report used COBOL as the host language. Regardless of the host programming language, the basic database manipulation commands of the net- work model remain the same. Although the network model and the object-oriented data model are both navigational in nature, the data structuring capability of the network model is much more elaborate and allows for explicit insertion/deletion/modification semantic specification. However, it lacks some of the desirable features of the object mod- els that we discussed in Chapter 11, such as inheritance and encapsulation of structure and behavior. 1. The complete chapter on the network data model and about the IDMS system from the second edition of this book is available at http://www.awl.com/cseng/titles/0-8053-1755-4. This appendix is an edited excerpt of that chapter. E–1 Elmasri_APPC Page 2 Monday, April 3, 2006 3:40 PM E–2 Appendix E / An Overview of the Network Data Model E.1 Network Data Modeling Concepts There are two basic data structures in the network model: records and sets. -
Spatial Data Extension for Cassandra Nosql Database
Ben Brahim et al. J Big Data (2016) 3:11 DOI 10.1186/s40537-016-0045-4 RESEARCH Open Access Spatial data extension for Cassandra NoSQL database Mohamed Ben Brahim1* , Wassim Drira1, Fethi Filali1 and Noureddine Hamdi2 *Correspondence: [email protected] Abstract 1 Qatar Mobility Innovations The big data phenomenon is becoming a fact. Continuous increase of digitization Center, Qatar Science and Technology Park, and connecting devices to Internet are making current solutions and services smarter, 210531 Doha, Qatar richer and more personalized. The emergence of the NoSQL databases, like Cassandra, Full list of author information with their massive scalability and high availability encourages us to investigate the is available at the end of the article management of the stored data within such storage system. In our present work, we harness the geohashing technique to enable spatial queries as extension to Cassandra query language capabilities while preserving the native syntax. The developed frame- work showed the feasibility of this approach where basic spatial queries are under- pinned and the query response time is reduced by up to 70 times for a fairly large area. Keywords: Big data, Spatial query, Geohash, Cassandra DB, NoSQL databases Background The proliferation of mobile applications and the widespread of hardware sensing devices increase the streamed data towards the hosting data-centers. This increase causes a flooding of data. Taking benefits from these massive dataset stores is a key point in cre- ating deep insights for analysts in order to enhance system productivity and to capture new business opportunities. The inter-connected systems are sweeping almost all sec- tors forming what’s called today Internet of Things. -
Studies and Analysis of Popular Database Models
Pramod Singh et al, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.5, May- 2015, pg. 834-838 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 4, Issue. 5, May 2015, pg.834 – 838 RESEARCH ARTICLE Studies and Analysis of Popular Database Models Dr. P. K. Rai Prof. I/C, BCA & Head, Computer Centre APSU Rewa Pramod Singh Research Scholar M.G.C.G.V Chitrakoot Satna (M.P), Satna, India; Email: [email protected] ABSTRACT: Data is most valuable part of today world because it is used in everyday of life from a single individual to large organizations. To make the selection and maintenance easy and support strong data protection it is stored in a data model. A data model is a repository of subjectively selected and adapted operational data which can successfully answer any, statistical, complex or analytical queries. Nowadays the database is shared by a globally competitive environment where large amounts of data are required to be processed faster and efficient way .data model keep a subject oriented data which gives the support for management's decision making process. This paper discusses in detail four existing database models like Hierarchical, network, relational and object oriented database and does an analysis to understand the advantages and disadvantages of these database models. Keywords: RDB, OODB. Introduction A Database is an important part of any organization because all information is store in the database where all users can easily access this information in well organized manner. -
Issue 8, April 2020
Issue 8, April 2020 INDEX Sl.no Title Page.no 1 Insight into Temporal and Spatial Database. 1 2 Hadoop Database. 2 3 Orient Db. 3 4 Oracle Autonomous Database. 4 5 Cloud Database. 6 6 Postgresql. 8 7 Data mining. 9 8 Mongo Db 12 9 Solarwinds Database Performance Analyzer (DPA). 13 10 Transaction Process of Database. 15 11 Couch Database. 17 12 Mongo Database. 18 13 Business Intelligence and Database. 19 14 Cloud Database. 21 INSIGHT INTO TEMPORAL AND SPATIAL DATABASE CHAITHRA R (19MCA04) A database is collection of data which are related to each other. Everyone involves in day to day use of database, like for instance our brain requires some data to store in and retrieve (remember). Some of real world examples of database are mobile data backups, Google services like text searches, maps, Gmail etc. Temporal and Spatial database are also one of the types of database. A temporal database stores time related data, it provides data types and stores information related to time i.e., past, present and future time. There are two types of time one is validate time-time period when a fact is true in real world and other is transaction time-time period when a fact stored in database was known and combination of both is a bi-temporal data. This type of database is used for insurance (accident and claims histories), banking (credit and debit histories), health care(stock prices) and scientific data(data about experiments) which are all time dependent data. A spatial database is optimized for storing and querying of data related to objects in a geometric space. -
The Network Model
Unit 5 The Network Model 5.1 The Network Model 5.2 IDMS Unit 5 The Network Model Data Modeling Issue . Issue: How to represent entities and relationships? . Two major paradigms S P SP • Relational Hierarchical • Graph { Network . E.g., a 'pile' of data • John, 25, NCTU, CS Dept, ... 01 • Mary, 22, NCTU, IS Dept, ... 02 02 02 02 .. 03 03 COBOL 01 data-record 01 02 name PIC X(6) 02 02 02 02 02 age PIC 9(2) 0303 02 univ PIC X(4) 02 dept PIC X(3) Wei-Pang Yang, Information Management, NDHU Unit 5 The Network Model 5-2 Model 1: Relational . Model 1: Relational - Decomposition (normalization issue) Emp Dept Project Wei-Pang Yang, Information Management, NDHU Unit 5 The Network Model 5-3 Model 2: Hierarchical Dept Emp Project Wei-Pang Yang, Information Management, NDHU Unit 5 The Network Model 5-4 Model 3: Network . Model 3: Network proposed by CODASYL Dept 1:M M : M Project Emp 1:M 1:M E-P Wei-Pang Yang, Information Management, NDHU Unit 5 The Network Model 5-5 5.1 The Network Model . Data Structure Consider the 'supplier-and-parts‘ database S P SP S# SNAME STATUS CITY P# PNAME COLOR WEIGHT CITY S# P# QTY } Structure S1 Smith 20 London P1 Nut Red 12 London S1 P1 300 S2 Jones 10 Paris P2 Bolt Green 17 Paris S1 P2 200 S3 Blake 30 Paris P3 Screw Blue 17 Rome S1 P3 400 S4 Clark 20 London P4 Screw Red 14 London S1 P4 200 S5 Adams 30 Athens P5 Cam Blue 12 Paris S1 P5 100 P6 Cog Red 19 London S1 P6 100 Sample values S2 P1 300 S2 P2 400 S3 P2 200 S4 P2 200 S4 P4 300 S4 P5 400 Wei-Pang Yang, Information Management, NDHU Unit 5 The Network Model 5-6 The Network Model: Sets and Structure Network Model a set of records (Record types): entities . -
Real Time and Spatiotemporal Data Indexing for Sensor Based Databases
3 . Management, analysis and integration Real time and spatiotemporal data indexing for sensor based databases S. Servigne, LIRIS: Research Center for Images and Information Systems, INSA, Bât. Blaise Pascal, 20 avenue Einstein, 69621 Villeurbanne Cedex, France G. Noel, LIRIS: Research Center for Images and Information Systems, INSA, Bât. Blaise Pascal, 20 avenue Einstein, 69621 Villeurbanne Cedex, France ABSTRACT: The most important aspect of a system for emergency response is time. So it is necessary to organise fast storage of newly arriving data into databases, fast search of data, maintenance of time sequences, robustness of the systems. All of these processes have to be real time. Another important aspect for environmental risk monitoring for example, is to allow real- time queries based on spatiotemporal attributes. Fastest accesses to the most recent data are also necessary. In addition, real-time data collected into real time database from sensors require fast and efficient management to avoid main memory saturation. The paper discusses real-time spatiotemporal indexing schemes and real time management of main memory. Some possible solutions for real time spatiotemporal data indexing are presented to allow real time and fast data structuring. The proposed index methods support queries privileging recent data. One of the presented indexing solutions is dedicated to real time spatiotemporal data issued from fixed sensors. Another one is dedicated to real time spatiotemporal data collected from agile sensors. Finally, an outline solution concerning management of real time memory saturation according to the significance of data is presented. 1 INTRODUCTION. Sensor networks are now common and usually used for many applications especially concerning risk monitoring. -
An ETL for Integrating Trajectory Data a Medical Delegate Activities Use Case Study
International Conference on Automation, Control, Engineering and Computer Science (ACECS'14) Proceedings - Copyright IPCO-2014, pp.138-147 ISSN 2356-5608 An ETL for Integrating Trajectory Data A Medical Delegate Activities Use Case Study Assawer Zekri Jalel Akaichi Computer science department. BESTMOD Laboratory, Computer science department. BESTMOD Laboratory, High Institute of Management, University of Tunis, High Institute of Management, University of Tunis, 41, City Bouchoucha, 2000 Bardo, Tunis, Tunisia 41, City Bouchoucha, 2000 Bardo, Tunis, Tunisia [email protected] [email protected] Abstract — Extract, Transfrom and Load (ETL) represent the Finally, the cleaned data are stored in data warehouses or data core of Business Intelligence (BI). They are responsible for marts. So, in this study, we focus our research on ETL. extracting data from heterogeneous sources, transforming and Nowadays, the area of trajectory represents a very active loading them into homogenous targets such as data warehouses. research topic. So, the advent of ubiquitous systems and Today, the spatiotemporal and trajectory data warehouses that positioning technologies that are increasingly widespread like represent an extension of the classical ones are fed by data coming from the pervasive systems which are equipped with GPS, RFID and radar allow the real-time monitoring of the positioning technologies. These latter cause an explosive growth movement of mobile objects and generate a sequence of of geo-located data which enable tracking continuously the trajectory data that income in unbounded way. The processing movements of mobile objects in a time sequence. These data are of these data arriving in real time, at high speed and in a characterized by their complex structures including continuous manner is difficult.