Showing papers on "Spatiotemporal database published in 2017"

ST-hash: An efficient spatiotemporal index for massive trajectory data in a NoSQL database

Abstract: With the development of positioning technologies and the increasing popularity of location-aware devices, large volumes of trajectory data have been accumulated. However, efficient management and access to massive trajectory data remains a big challenge. The emerging NoSQL database has provided a promising solution for this challenge. But most of the current NoSQL databases do not support direct spatiotemporal indexing of massive trajectory data. This paper presents a novel trajectory indexing method to accelerate time-consuming spatiotemporal queries of massive trajectory data. This method extends the widely-used GeoHash algorithm to satisfy the requirements for both high-frequent updates and common trajectory query operations, e.g. exact point query and spatiotemporal range query. This ST-Hash index was implemented and evaluated in a NoSQL database (MongoDB). Experimental results show that this proposed ST-Hash index can greatly improve the query performance and exhibits robust performance scalability over different input data sizes.

Computational Movement Analysis

Abstract: This SpringerBrief discusses the characteristics of spatiotemporal movement data, including uncertainty and scale. It investigates three core aspects of Computational Movement Analysis: Conceptual modeling of movement and movement spaces, spatiotemporal analysis methods aiming at a better understanding of movement processes (with a focus on data mining for movement patterns), and using decentralized spatial computing methods in movement analysis. The author presents Computational Movement Analysis as an interdisciplinary umbrella for analyzing movement processes with methods from a range of fields including GIScience, spatiotemporal databases and data mining. Key challenges in Computational Movement Analysis include bridging the semantic gap, privacy issues when movement data involves people, incorporating big and open data, and opportunities for decentralized movement analysis arising from the internet of things. The interdisciplinary concepts of Computational Movement Analysis make this an important book for professionals and students in computer science, geographic information science and its application areas, especially movement ecology and transportation research.

Relational database and graph database: A comparative analysis

Abstract: Since 1970, relational database models have been in use for storing, manipulating and retrieving data. The importance of relational databases is going to decrease due to the exponential growth of data as it is difficult t o work with large number of joining tables. For such kinds of problems, one of the best solutions is to use graph database for storing data. The graph database can be used to store highly connected data. In this article, we are going to put forward comparison between relational database and graph database with reference to an experiment performed.

Reengineering Fuzzy Spatiotemporal UML Data Model Into Fuzzy Spatiotemporal XML Model

Abstract: Spatiotemporal information and application have received considerable attention. Spatiotemporal information is often imprecise or uncertain; therefore, the establishment of a reasonable and effective fuzzy spatiotemporal data model is vital. The traditional tool-based modeling method cannot meet the needs of fuzzy spatiotemporal data modeling. XML has become the standard of Web data representation and exchange. Based on the fuzzy set and probability theory, this paper analyzes the characteristics of fuzzy spatiotemporal data and establishes a fuzzy spatiotemporal XML data model. We present a fuzzy spatiotemporal UML data model and provide the rules for converting the fuzzy spatiotemporal UML data model to the fuzzy spatiotemporal XML model. We demonstrate our model and the conversion by applying them to a meteorological event.

Geo-event processor

Abstract: Embodiments of the present invention provide a geoevent processor with the ability to enable real-time GIS (Geographic Information System). The geoevent processor has connectors that enable ingesting real-time data from a wide variety of sources. Those can include social media, in-vehicle GPS devices, military formats, and many more. Once connected, the geoevent processor provides the ability to perform continuous analysis and processing as the data is received. A spatiotemporal database is used to store real time observational data by location and time.

FSPTwigFast: Holistic twig query on fuzzy spatiotemporal XML data

Abstract: With spatiotemporal applications increasing, a large amount of spatiotemporal data emerges. Because temporal and spatial attributes are often vague, research on fuzzy spatiotemporal data, especially querying fuzzy spatiotemporal data, has attracted a lot of attention. However, although fuzzy logic is incorporated in querying fuzzy spatiotemporal data and querying fuzzy data in XML, relatively little work has been carried out in querying fuzzy spatiotemporal data in XML. In this paper, we propose an algorithm, called FSPTwigFast, to match fuzzy spatiotemporal XML twig pattern. We represent fuzzy spatiotemporal data by adding temporal and spatial attributes associating with fuzziness in crisp data. We extend Dewey code to mark fuzzy spatiotemporal data for special process and determine structure relationship of fuzzy spatiotemporal nodes in XML documents. Our technique uses streams to store leaf nodes in XML document corresponding to leaf query nodes, which are filtered to delete unmatched nodes. After filtering, output lists are built for every matched leaf node. Finally, the experimental results demonstrate the performance advantages of our approach.

An Integrated Solar Database (ISD) with Extended Spatiotemporal Querying Capabilities

Abstract: Over the last decade, the volume of solar big data have increased immensely. However, the availability and standardization of solar data resources has not received much attention primarily due to the scattered structure among different data providers, lack of consensus on data formats and querying capabilities on metadata. Moreover, there is limited access to the derived solar data such as image parameters extracted either from solar images or tracked solar events. In this paper, we introduce the Integrated Solar Database (ISD), which aims to integrate the heterogeneous solar data sources. In ISD, we store solar event metadata, tracked and interpolated solar events, compressed solar images, and texture parameters extracted from high resolution solar images. ISD offers a rich variety of spatiotemporal and aggregate queries served via a web Application Program Interface (API) and visualized through a web interface.

Semantic Trajectory Knowledge Discovery: A Promising Way to Extract Meaningful Patterns from Spatiotemporal Data

Abstract: Spatiotemporal data mining studies the field of discovering interesting patterns from large spatiotemporal databases. Although these databases generate a huge volume of data daily from satellite images and mobile sensors like GPS, among these data we find first spatiotemporal and geographical data; secondly, the trajectories browsed by moving objects in some time intervals. Combination of these types of data leads to producing semantic trajectory data. Enriching trajectories with semantic geographical information leads to ease queries, analysis, and mining, in order to give more meaning to behaviors potentially extracted from trajectories. Therefore, applying mining techniques on semantic trajectories continue to prove to be a success story in discovering useful and nontrivial behavioral patterns of moving objects. The purpose of this paper is to make an overview of spatiotemporal knowledge discovery (STKD) and techniques recently used to extract knowledge from spatiotemporal data based on analysis of recent literature. Then leading towards a deeper analysis about semantic trajectory knowledge discovery as a specified field from STKD that integrates trajectory sample points with geographical data before applying mining techniques in order to extract behavioral knowledge from semantic trajectories which can be more useful and significant for the application users.

LabbookDB: A Wet-Work-Tracking Database Application Framework

Abstract: LabbookDB is a relational database application framework for life sciences—providing an extendable schema and functions to conveniently add and retrieve information, and generate summaries. The core concept of LabbookDB is that wet work metadata commonly tracked in lab books or spreadsheets is more efficiently and more reliably stored in a relational database, and more flexibly queried. We overcome the flexibility limitations of designed-for-analysis spreadsheets and databases by building our schema around atomized physical object interactions in the laboratory (and providing plottingand/or analysisready dataframes as a compatibility layer). We keep our database schema more easily extendable and adaptable by using joined table inheritance to manage polymorphic objects and their relationships. LabbookDB thus provides a wet work metadata storage model excellently suited for explorative ex-post reporting and analysis, as well as a potential infrastructure for automated wet work

Object data subscription method based on spatiotemporal database

Abstract: The invention relates to an object data subscription method based on a spatiotemporal database, comprising an organizing process and a subscription process of data objects. The organizing process comprises a process of constructing a database of the data objects; the subscription process is a step of actively pushing information to the client associated with the database according to at least one data record change of the spatiotemporal database so that the user can know in time the running state of the desired object to be managed. The spatiotemporal database of the objects at least includes interaction among the real-time database, and the historical database and the planning database of the object data interacting with each other so as to timely transmit and monitor the running state of the objects during the automatic operation thereof.

A Comparative Study of Frequent and Maximal Periodic Pattern Mining Algorithms in Spatiotemporal Databases

Abstract: Detecting regular and efficient cyclic models is the demanding activity for data analysts due to unstructured, vigorous and enormous raw information produced from web. Many existing approaches generate large candidate patterns in the occurrence of huge and complex databases. In this work, two novel algorithms are proposed and a comparative examination is performed by considering scalability and performance parameters. The first algorithm is, EFPMA (Extended Regular Model Detection Algorithm) used to find frequent sequential patterns from the spatiotemporal dataset and the second one is, ETMA (Enhanced Tree-based Mining Algorithm) for detecting effective cyclic models with symbolic database representation. EFPMA is an algorithm grows models from both ends (prefixes and suffixes) of detected patterns, which results in faster pattern growth because of less levels of database projection compared to existing approaches such as Prefixspan and SPADE. ETMA uses distinct notions to store and manage transactions data horizontally such as segment, sequence and individual symbols. ETMA exploits a partition-and-conquer method to find maximal patterns by using symbolic notations. Using this algorithm, we can mine cyclic models in full-series sequential patterns including subsection series also. ETMA reduces the memory consumption and makes use of the efficient symbolic operation. Furthermore, ETMA only records time-series instances dynamically, in terms of character, series and section approaches respectively. The extent of the pattern and proving efficiency of the reducing and retrieval techniques from synthetic and actual datasets is a really open & challenging mining problem. These techniques are useful in data streams, traffic risk analysis, medical diagnosis, DNA sequence Mining, Earthquake prediction applications. Extensive investigational outcomes illustrates that the algorithms outperforms well towards efficiency and scalability than ECLAT, STNR and MAFIA approaches.

Modelling of Graph Databases

Abstract: Comparing graph databases with traditional, e.g., relational databases, some important database features are often missing there. Particularly, a graph database schema including integrity constraints is mostly not explicitly de ned, also a conceptual modelling is not used. It is hard to check a consistency of the graph database, because almost no integrity constraints are de ned or only their very simple representatives can be speci ed. In the paper, we discuss these issues and present current possibilities and challenges in graph database modelling. We focus also on integrity constraints modelling and propose functional dependencies between entity types, which reminds modelling functional dependencies known from relational databases. We show a number of examples of often cited GDBMSs and their approach to database schemas and ICs speci cation. Also a conceptual level of a graph database design is considered. We propose a su cient conceptual model based on a binary variant of the E-R model and show its relationship to a graph database model, i.e. a mapping conceptual schemas to database schemas. An alternative based on the conceptual functions called attributes is presented.

Spatiotemporal Data Organization and Application Research

Abstract: . Organization and management of spatiotemporal data is a key support technology for intelligence in all fields of the smart city. The construction of a smart city cannot be realized without spatiotemporal data. Oriented to support intelligent applications，this paper proposes an organizational model for spatiotemporal data, and details the construction of a spatiotemporal big data calculation, analysis, and service framework for highly efficient management and intelligent application of spatiotemporal data for the entire data life cycle.

Spatiotemporal database of US congressional elections, 1896-2014.

Abstract: High-quality historical data about US Congressional elections has long provided common ground for electoral studies. However, advances in geographic information science have recently made it efficient to compile, distribute, and analyze large spatio-temporal data sets on the structure of US Congressional districts. A single spatio-temporal data set that relates US Congressional election results to the spatial extent of the constituencies has not yet been developed. To address this, existing high-quality data sets of elections returns were combined with a spatiotemporal data set on Congressional district boundaries to generate a new spatio-temporal database of US Congressional election results that are explicitly linked to the geospatial data about the districts themselves. Machine-accessible metadata file describing the reported data (ISA-Tab format)

Special issue on spatial and temporal database management

Abstract: This special issue contains six contributions selected as best papers from the 14th International Symposium on Spatial and Temporal Databases (SSTD) held in Hong Kong in August 2015. SSTD is an established series of events that continuously demonstrate how much spatial and temporal data are still popular and active research domains of interest, with still many research issues to address and application areas to explore. Over the past few years the range of topics addressed has evolved, with the emergence of big spatial and temporal data, location-based services, environmental and urban sensors to mention a few examples, thus generating a lot of new research problems and novel methods still to be explored in order to deliver appropriate data manipulation and interfaces at the user level. The selected papers published in this special issue are representatives of the recent developments of spatial-temporal data management. One popular form of mobile data is trajectories each representing a sequence of sampled points of a moving object. The paper by Shuyao Qi et al. (Snapshot and Continuous Points-based Trajectory Search) performs data analysis on these trajectories. On the other hand, shortest distance computation is one important topic in the literature. The paper by Alexandros Efentakis et al. (Hub Labels on the database for large-scale graphs with the COLD framework) studies how to use hub labels for shortest-distance computation. Recently, privacy has attracted a lot of attention due to its importance. This privacy issue was studied by Mihai Maruseac et al. (Privacy-Preserving Detection of Anomalous Phenomena in Crowdsourced Environmental Sensing using FineGrained Weighted Voting). The paper entitled ‘Knowledge Extraction from Crowdsourced Data for the Enrichment of Road Networks’ written by G. Josse et al. studied how to enrich the information of road networks by extracting knowledge from crowdsourced data. The paper Geoinformatica (2017) 21:667–668 DOI 10.1007/s10707-017-0307-0

Spatio-temporal data model for integrating evolving nation-leveldatasets

Abstract: . Ability to easily combine the data from diverse sources in a single analytical workflow is one of the greatest promises of the Big Data technologies. However, such integration is often challenging as datasets originate from different vendors, governments, and research communities that results in multiple incompatibilities including data representations, formats, and semantics. Semantics differences are hardest to handle: different communities often use different attribute definitions and associate the records with different sets of evolving geographic entities. Analysis of global socioeconomic variables across multiple datasets over prolonged time is often complicated by the difference in how boundaries and histories of countries or other geographic entities are represented. Here we propose an event-based data model for depicting and tracking histories of evolving geographic units (countries, provinces, etc.) and their representations in disparate data. The model addresses the semantic challenge of preserving identity of geographic entities over time by defining criteria for the entity existence, a set of events that may affect its existence, and rules for mapping between different representations (datasets). Proposed model is used for maintaining an evolving compound database of global socioeconomic and environmental data harvested from multiple sources. Practical implementation of our model is demonstrated using PostgreSQL object-relational database with the use of temporal, geospatial, and NoSQL database extensions.

Converting Temporal Relational Database into Temporal Object Relational Database

Abstract: This paper presents an approach for migrating existing temporal relational database (TRDB), into temporal object relational database (TORDB). This is done by enhancing a representation of a varying time database’s structure, in order to make hidden semantic explicit. In contrast to other studies, our main goal here is to offer a first and better solution to mentioned limits to existing works, in order to provide the efficient and the correct method for the translation from TRDB to TORDB. We are going to take an existing RDB using valid time features as input, enrich its metadata representation, and generate a new valid time data Model (NVTM), which captures the most important characteristics of temporal databases for conversion. From the NVTM, we will develop our TORDB design scheme in order to simplify the implementation of a temporal object. Through this UML profile, we precede to the last step, the creation of temporal object relational tables integrating valid time aspects.

Querying and Visualization of Moving Objects Using Constraint Databases

Abstract: Acknowledgements iii