Showing papers on "Spatiotemporal database published in 2014"

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.

Pre-join tags for entity-relationship modeling of databases

Abstract: Join-queried entities are selectively linked in an entity-relationship model of the database by defining a pre-join tag in the entity-relationship model of the database and selectively providing the pre-join tag between join-queried entities in the entity-relationship model of the database. Frequently join-queried entities in the entity-relationship model of the database may be identified, and a pre-join tag may be provided between the frequently join-queried entities. A non-relational database may be generated from the entity-relationship model by creating merged entities in the non-relational database based on the pre-join tags. A relational database may be generated from the entity-relationship model by selectively creating merged entities in the relational database based on the pre-join tags.

Spatiotemporal indexing techniques for efficiently mining spatiotemporal co-occurrence patterns

Abstract: In this paper, we investigate using specifically-designated spatiotemporal indexing techniques for mining cooccurrence patterns from spatiotemporal datasets with evolving polygon-based representations. Previously, suggested techniques for spatiotemporal pattern mining algorithms did not take spatiotemporal indexing techniques into account. We present a new framework for mining spatiotemporal co-occurrence patterns that can use various indexing techniques for efficiently accessing data. Two well-studied spatiotemporal indexing structures, Scalable and Efficient Trajectory Index (SETI) and Chebyshev Polynomial Indexing are currently implemented and available in our framework.

Indexing and querying moving objects with uncertain speed and direction in spatiotemporal databases

Abstract: Efficient processing of spatiotemporal queries over moving objects with uncertainty has become imperative due to the increasing need for real-time information in highly dynamic environments. Most of the existing approaches focus on designing an index structure for managing moving objects with uncertainty and then utilize it to improve the query performance. All the proposed indexes, however, have their own limitations. In this paper, we devote to developing an efficient index, named the Rlsd-tree, to index moving objects with uncertain speed and direction varying within respective known ranges. We design several pruning criteria combined with the Rlsd-tree to answer the probabilistic range queries. Moreover, two models, the sampling-based probability model and the ER-based probability model, are proposed to quantify the possibility of each object being the query result. Finally, a thorough experimental evaluation is conducted to show the merits of the proposed techniques.

Optimizing an index with spatiotemporal patterns to support GEOSS Clearinghouse

Abstract: A variety of Earth observation systems monitor the Earth and provide petabytes of geospatial data to decision-makers and scientists on a daily basis. However, few studies utilize spatiotemporal patterns to optimize the management of the Big Data. This article reports a new indexing mechanism with spatiotemporal patterns integrated to support Big Earth Observation (EO) metadata indexing for global user access. Specifically, the predefined multiple indices mechanism (PMIM) categorizes heterogeneous user queries based on spatiotemporal patterns, and multiple indices are predefined for various user categories. A new indexing structure, the Access Possibility R-tree (APR-tree), is proposed to build an R-tree-based index using spatiotemporal query patterns. The proposed indexing mechanism was compared with the classic R*-tree index in a number of scenarios. The experimental result shows that the proposed indexing mechanism generally outperforms a regular R*-tree and supports better operation of Global Earth Obs...

Towards an instrumental harmonization in the framework of LALINET: dataset of technical specifications

Abstract: The Latin American Lidar Network (LALINET) is the aerosol lidar network operating over South America. LALINET is now an operative network performing a schedule of routine measurements and, currently, is composed by 9 stations distributed over South America. The main objective of LALINET is to generate a consistent and statistically relevant database to enhance the understanding of the particle distribution over the continent and its direct and indirect influence on climate. The creation of an un-biased spatiotemporal database requires a throughout review of the network on two pillars: instrumentation and data processing. Because most of the LALINET systems are not series-produced instruments and, therefore, present large differences in configuration and capabilities, attempts for network harmonization and, consequently, optimization are mandatory. In this study a review of the current instrumental status of all LALINET systems is done and analyzed in detail in order to assess the potential performance of the network and to detect networking weaknesses.

An Event-driven Spatiotemporal Data Model (E-ST) Supporting Dynamic Expression and Simulation of Geographic Processes

Abstract: The wide use of various sensors makes real-time data acquisition possible. A new spatiotemporal data model, the Event-driven Spatiotemporal Data Model (E-ST), is proposed to dynamically express and simulate the spatiotemporal processes of geographic phenomena. In E-ST, a sensor object is introduced into the model as a flexible real-time data source. An event type that is generating and driving conditions is registered into a geographic object, so an event can not only express spatiotemporal change in a geographic object, but also drive spatiotemporal change in some geographic objects. As a dynamic GIS data model, the E-ST has five characteristics – Temporality and Spatiality, Real-time, Extendability, Causality, and Realizability. Described and realized in UML, a test-case deployment demonstrating the impact of urban waterlogging on traffic confirms that a spatiotemporal change process in a geographic phenomena is expressed and simulated by this model. Summarizing this work, four directions for future research are outlined.

gst-Store: An Engine for Large RDF Graph Integrating Spatiotemporal Information.

Abstract: In this paper, we present a spatiotemporal information integrated RDF data management system, called g st -Store. In g st -Store, some entities have spatiotemporal features, and some statements have valid time intervals and occurring locations. We introduce some spatiotemporal assertions into the SPARQL query language to answer the spatiotemporal range queries and join queries. Some examples are listed to demonstrate our demo.

Estimating Statistics for Generating Execution Plans for Database Queries

Abstract: A database system identifies missing statistics that is not available for processing database queries and determines the missing statistics. The database system generates execution plans for database queries. The database system requests certain statistical information for generating a database query. If the database system determines that the requested statistical information is not available, the database system stores information describing the requested statistical information as missing statistics. The missing statistics may identify one or more columns associated with tables processed by the database query. The database system performs statistical analysis of database tables to generate the missing statistics so that the statistical information is available for generating execution plans for subsequent queries. The database system may rank the missing statistics based on the number of times the missing statistics was identified by the database system while processing database queries.

Finding Frequent and Maximal Periodic Patterns in Spatiotemporal Databases towards Big Data

Abstract: Data mining used to find hidden knowledge from large amount of Databases. Periodic Pattern Mining is useful in Weather Forecasting, Fraud Detection and GIS Applications. In General, spatio-temporal pattern discovery process finds the partially ordered subsets of the event- types whose instances are located together and occur serially for a given collection of Boolean spatio-temporal event-types. Big Data concerns large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data is now rapidly expanding in all science and engineering domains, including physical, biological and bio-medical sciences. In this paper, a new framework is proposed to find spatiotemporal patterns from Big Data. Existing algorithms are well in computation of necessary patterns, but more problematic when they are applied to Big Data. Big Data is a new trend used to analyze the datasets that due to their large size and complexity, Developers cannot manage them with traditional current algorithms or data mining software tools. Big Data mining is the capability of extracting useful information from these large datasets or streams of data, that due to its volume, variety, and velocity, it was not possible before to do it. The Big Data challenge is becoming one of the most exciting opportunities for the next years. This Paper focuses on a broad overview of pattern mining algorithms and significance in Spatiotemporal Databases, its current status, trade-offs, and forecast to the big data pattern mining future.

Dissemination and geovisualization of territorial entities' history

Abstract: This paper describes an innovative solution for geovisualization of the demographic and administrative history of French municipalities, named “communes” in French. This solution allows for the open dissemination of such data. The challenge is to provide a web interface for unskilled users in order to help them understand complex information about the demographic evolution of French territories. Our approach combines interactive thematic, spatial, and temporal views. We describe our architecture, based on open-source technologies, and the organization of this imperfect geo-historical information in our spatiotemporal database. Our second contribution concerns the concept of an acquaintance graph that has been used to obtain an efficient design with good performance in our geovisualization website.

Finding Frequent and Maximal Periodic Patterns in Spatiotemporal Databases for Shifted Instances

Abstract: Data mining used to find hidden knowledge from large amount of Databases. Periodic Pattern Mining is useful in Weather Forecasting, Fraud Detection and GIS Applications. In General, spatio-temporal pattern discovery process finds the partially ordered subsets of the event-types whose instances are located together and occur serially for a given collection of Boolean spatio-temporal event-types. Big Data concerns large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data is now rapidly expanding in all science and engineering domains, including physical, biological and bio-medical sciences. In this paper, a new framework is proposed to find spatiotemporal patterns from Big Data. Existing algorithms are well in computation of necessary patterns, but more problematic when they are applied to Big Data. Big Data is a new trend used to analyse the datasets that due to their large size and complexity, Developers cannot manage them with traditional current algorithms or data mining software tools. Big Data mining is the capability of extracting useful information from these large datasets or streams of data, that due to its volume, variety, and velocity, it was not possible before to do it. The Big Data challenge is becoming one of the most exciting opportunities for the next years. This Paper focuses on a broad overview of pattern mining algorithms and significance in Spatiotemporal Databases, its current status, trade-offs, and forecast to the big data pattern mining future.

Plain move predicate and its consistency concerning the moving agents in a network

Abstract: Advances in GIS and databases for dealing with spatiotemporal frameworks are leading to efficient querying, analyzing, and reasoning about moving objects/agents However, contemporary frameworks on spatiotemporal logics are usually limited to qualitative approaches, such as the numerous studies on spatiotemporal databases that focus on observations, ignoring the intended movements of the agents Moving object databases, on the other hand, can handle queries about the location, velocity, and time by assuming some agents to be targets However, reasoning about the plan of moving agents, especially on the network and the achievability of such a plan, still remains a challenge Studies on vehicle routing are often about the centralized planning of moving agents from scratch and do not deal with intended plans Based on a plain move predicate, this paper presents a generic framework that can be used for representing and reasoning about plans of moving agents Concepts from motion, network structure, graph theory, predicate logic, and constraint satisfaction are used to create the framework We have also provided efficient algorithms for checking the consistency of the movement and extracting compatible plans along with some discussions on computational analysis, logical deduction, and flexibility Finally, we have demonstrated the application of the methodology by developing NETwork-based Move Atoms Planning System NETMAPS The experiments show how NETMAPS can overcome an inconsistent movement plan and deliver advantageous suggestions to an executive agent

Technical Report: Parallel Distance Threshold Query Processing for Spatiotemporal Trajectory Databases on the GPU.

Abstract: Processing moving object trajectories arises in many application domains and has been addressed by practitioners in the spatiotemporal database and Geographical Information System communities. In this work, we focus on a trajectory similarity search, the distance threshold query, which finds all trajectories within a given distance d of a search trajectory over a time interval. We demonstrate the performance of a multithreaded implementation which features the use of an R-tree index and which has high parallel efficiency (78%-90%). We introduce a GPGPU implementation which avoids the use of index-trees, and instead features a GPU-friendly indexing method. We compare the performance of the multithreaded and GPU implementations, and show that a speedup can be obtained using the latter. We propose two classes of algorithms, SetSplit and GreedySetSplit, to create efficient query batches that reduce memory pressure and computational cost on the GPU. However, we find that using fixed-size batches is sufficiently efficient in practice. We develop an empirical performance model for our GPGPU implementation that can be used to predict the response time of the distance threshold query. This model can be used to pick a good query batch size.

Navigation database customization

Abstract: Custom geographic databases are created by analyzing a geographic database to determine database structure elements The database structure elements may involve road attributes or points of interest Database structure elements are then selected to include in a customized geographic database A customized geographic database is created that includes the selected database structure elements

Performance estimation of selecting and inserting procedures in the structure-independent database

Abstract: Static databases are a class of databases that can solve a problem to add new entities and attributes without changing their physical structure. Structure-independent database is relates to this class. To use it successfully we need to estimate its performance in comparison with a relational database. This article presents structures of selection and inserting procedures in relational and structure-independent databases. As a result, computational experiments were held and quantitative estimation is given.

Wildlife tracking data management: chances come from difficulties

Abstract: In recent years, new wildlife tracking and telemetry technologies have become available, leading to substantial growth in the volume of wildlife tracking data. In the future, one can expect an almost exponential increase in collected data as new sensors are integrated into current tracking systems. A crucial limitation for efficient use of telemetry data is a lack of infrastructure to collect, store and efficiently share the information. Large data sets generated by wildlife tracking equipment pose a number of challenges: to cope with this amount of data, a specific data management approach is needed, one designed to deal with data scalability, automatic data acquisition, long-term storage, efficient data retrieval, management of spatial and temporal information, multi-user support and data sharing and dissemination. The state-of-the-art technology to meet these challenges are relational database management systems (DBMSs), with their dedicated spatial extension. DBMSs are efficient, industry-standard tools for storage, fast retrieval and manipulation of large data sets, as well as data dissemination to client programs or Web interfaces. In the future, we expect the development of tools able to deal at the same time with both spatial and temporal dimensions of animal movement data, such as spatiotemporal databases.

Mobility Database Management

Abstract: Adding temporal information, as an extra attribute in spatial databases, is not as straightforward as it may appear at a first glance. Time is not yet another dimension besides the two (or three, in some applications) spatial dimensions; monotonicity, for example, is a key difference. Could we adopt “as-is” methods and techniques for spatial databases, such as the ones outlined in Chap. 2? The answer is rather not, and this has been argued extensively in the spatiotemporal database literature. Therefore, novel data types (e.g., moving points), query processing techniques (e.g., “search for trajectories that ‘entered’ an area during a timeframe” or “search for trajectories that are ‘similar’ with respect to a reference trajectory”) and indexing methods (most probably, extensions of the well-known R-tree) have been explored. This chapter surveys the above aspects, which are essential components of a database system targeting at efficiently handing mobility data. In particular, interesting location- and mobility-aware queries are overviewed. Then, at physical level, selected indexing and query processing techniques that have been designed to efficiently support the above models and query types are presented.

Management of history of the rotation of agricultural parcels through open-source tools

Abstract: This paper presents how an open-source GIS can help to manage a spatiotemporal database dedicated to the long-term monitoring of an agricultural landscape. Based on the space-time composite model (Langran & Chrisman 1998), the system particularly focus on crop rotations and change of shape in agriculture parcels. In order to improve the performance and the flexibility of the system, a migration to an open-source solution was performed. At first, we highlight benefits of this approach as well as its limitations in terms of the ability to integrate new data sources. Then, we propose a new architecture that would facilitate the adaptation of software solutions to the extensions of our data model.

Simulation of Data Mining System Design in Database

Abstract: The design of data mining system in database is researched. Vast amounts of information contained in the database, and the data show the diversity of characteristics, resulting in lower efficiency of data mining in database, which database brought greater difficulties to information query. To avoid these shortcomings, database performance optimization method based on cloud computing is proposed. The model of cloud computing data relationship is established to describe the connection between related data inthe database, thus providing the basis for data query. The load state of data nodes is calculated to enable rapid information inquiryin the database. Experimental results show that using this algorithm to optimize data inquiry in database can improve the efficiency of informationinquiry indatabase effectively.

Spatiotemporal Data Model for Multi-Factor Geological Process Analysis with Case Study

Abstract: Although current event-based or event-driven spatiotemporal data model can express causality of space-time changes during the spatiotemporal process, it cannot express many interior factors that causing event changes or relationship between events, even hard to extract event types from specific process. A spatiotemporal data model for multi-factor geological process analysis is proposed to better support complex process simulation and analysis in this article. Corresponding to the expression of geometry, attribution, spatial relationship, semantics and behavior of geological spatial objects, this conceptual model is composed of spatiotemporal class, geological object class, event class, status class, feature class, observation class and geological model class. The validity of this model is tested by the monitoring on typical coal mine production of China. In this system discrimination model, synthesized anomaly information can be used in mine safety production decision making based on spatiotemporal analysis.

Management of different database systems and/or environments

Abstract: A database request can be processed at least partly based on one or more differences between multiple database systems and/or environments. The differences can, for example, include differences between one or more database capabilities respectively provided by the multiple database systems, differences between the representation of data in the multiple database systems, and differences in the interfaces for accessing the multiple database systems.

Database slicing on relational databases

Abstract: Many software systems today use databases to permanently store their data Testing, bug finding and migration are complex problems in the case of databases that contain many records Here, our method can speed up these processes if we can select a smaller piece of the database (called a slice) that contains all of the records belonging to the slicing criterion The slicing criterion might be, for example, a record which gives rise to a bug in the program Database slicing seeks to select all the records belonging to a specific slicing criterion Here, we introduce the concept of database slicing and describe the algorithms and data structures necessary for slicing a given database We define the Table-based and the Record-based slicing algorithms and we empirically evaluate these methods in two scenarios by applying the slicing to the database of a real-life application and to random generated database content

Database Constraints Applied to Metabolic Pathway Reconstruction Tools

Abstract: Our group developed two biological applications, Biblio-MetReS and Homol-MetReS, accessing the same database of organisms with annotated genes. Biblio-MetReS is a data-mining application that facilitates the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the process(es) of interest and their function. It also enables the sets of proteins involved in the process(es) in different organisms to be compared directly. The efficiency of these biological applications is directly related to the design of the shared database. We classified and analyzed the different kinds of access to the database. Based on this study, we tried to adjust and tune the configurable parameters of the database server to reach the best performance of the communication data link to/from the database system. Different database technologies were analyzed. We started the study with a public relational SQL database, MySQL. Then, the same database was implemented by a MapReduce-based database named HBase. The results indicated that the standard configuration of MySQL gives an acceptable performance for low or medium size databases. Nevertheless, tuning database parameters can greatly improve the performance and lead to very competitive runtimes.