Spatial Temporal Data Management

The accurate prediction of human movement trajectory has a variety of benefits for many applications such as optimizing day-today home movements of old or disabled people to minimize their routine efforts, etc. In order to perform human movement prediction, large amount of historical positioning data from sensors has to be collected and mined by analyzing different human sequential movement prediction approaches and their limitations. In this proposed work, Apriori algorithm which predicts the human movement sequence patterns in indoor environment. The Apriori is integrated into Bagged J48 Machine learning algorithm which results in an ensemble model to predict the future human movement patterns. These patterns are mined based on spatial, temporal and social data which add more accuracy to our prediction. This model also performs clustering mechanism to detect the abnormal patterns.

Abstract: Time is one of the most difficult aspects to handle in real world applications such as database systems. Relational database management systems proposed by Codd offer very little built-in query language support for temporal data management. The model itself ...

Crime analysts attempt to identify regularities in police recorded crime data with a central view of disrupting the patterns found. One common method for doing so is hotspot mapping, focusing attention on spatial clustering as a route to crime reduction (Chainey & Ratcliffe, 2005; Clarke & Eck, 2003). Despite the widespread use of this analytical technique, evaluation tools to assess its ability to accurately predict spatial patterns have only recently become available to practitioners (Chainey, Tompson, & Uhlig, 2008). Crucially, none has examined this issue from a spatio-temporal standpoint. Given that the organisational nature of policing agencies is shift based, it is common-sensical to understand crime problems at this temporal sensitivity, so there is an opportunity for resources to be deployed swiftly in a manner that optimises prevention and detection.

This paper tests whether hotspot forecasts can be enhanced when time-of-day information is incorporated into the analysis. Using street crime data, and employing an evaluative tool called the Predictive Accuracy Index (PAI), we found that the predictive accuracy can be enhanced for particular temporal shifts, and this is primarily influenced by the degree of spatial clustering present. Interestingly, when hotspots shrank (in comparison with the all-day hotspots), they became more concentrated, and subsequently more predictable. This is meaningful in practice; for if crime is more predictable during specific timeframes, then response resources can be used intelligently to reduce victimisation.

We address the problem of registering and discovering sensors, sensor services and other related resources in the Semantic Sensor Web. We show how to develop a semantic registry for the storage and manipulation of metadata describing such resources using an implementation of the data model stRDF and the query language stSPARQL. stRDF extends RDF with the ability to represent spatial and temporal metadata using linear constraints. stSPARQL extends SPARQL for querying stRDF metadata. Together they provide a natural modeling framework for the problem at hand, since spatial and temporal metadata figure prominently in the description of sensors and related resources or services. We present Strabon, a storage and query evaluation module for stRDF/stSPARQL for the architecture of the EU FP7 project SemsorGrid4Env and we show how to use Strabon to implement a semantic registry.

With the rapid growth of mobile devices and applications , geo-tagged data has become a major workload for big data storage systems. In order to achieve scalability, existing solutions build an additional index layer above general purpose distributed data stores. Fulfilling the semantic level need, this approach, however, leaves a lot to be desired for execution efficiency, especially when users query for moving objects within a high resolution geometric area, which we call geometry queries. Such geometry queries translate to a much larger set of range scans, forcing the backend to handle orders of magnitude more requests. Moreover, spatial-temporal applications naturally create dynamic workload hotspots 1 , which pushes beyond the design scope of existing solutions. This paper presents Pyro, a spatial-temporal big-data storage system tailored for high resolution geometry queries and dynamic hotspots. Pyro understands geome-tries internally, which allows range scans of a geometry query to be aggregately optimized. Moreover, Pyro employs a novel replica placement policy in the DFS layer that allows Pyro to split a region without losing data locality benefits. Our evaluations use NYC taxi trace data and an 80-server cluster. Results show that Pyro reduces the response time by 60X on 1km×1km rectangle geometries compared to the state-of-the-art solutions. Pyro further achieves 10X throughput improvement on 100m × 100m rectangle geometries 2 .

Skyline queries return the set of non-dominated tuples, where a tuple is dominated if there exists another with better values on all attributes. In the past few years the problem has been studied extensively, and a great number of external memory algorithms have been proposed. We thoroughly study the most important scan-based methods, which perform a number of passes over the database in order to extract the skyline. Although these algorithms are specifically designed to operate in external memory, there are many implementation details which are neglected, as well as several design choices resulting in different favors for these basic methods. We perform an extensive experimental evaluation using real and synthetic data. We conclude that specific design choices can have a significant impact on performance. We also demonstrate that, contrary to common belief, simpler skyline algorithm can be much faster than methods based on pre-processing

Sports facilities are built objects and outdoor surfaces in the built environment, with significant impacts and effects on society, economy and environment. High-quality sports facilities, their proper spatial distribution and accessibility are important factors in the development of sport and sports-related activities and influence the quality of life. Data on sports facilities and their spatial distribution are a measure for the quality of sports areas. They represent the basis for strategies for the development of sports facilities on local and national levels, their placement in the built environment, and support the development of other
sports-related activities. This article highlights the definition of a sports facility and its importance to the built environment and society. The survey includes an analysis of spatial and other databases on sports facilities in Slovenia and an assessment of their quality. A variety of methods and procedures was used
to merge and complement the existing databases of sports facilities. The article presents spatial and other analyses and thematic maps of sports facilities by municipalities and statistical regions. It also presents a correlation between the funds Slovenia invested in sports facilities, and the development coefficient of
municipalities. Indoor and outdoor sports facilities in individual municipalities are also presented.

This paper presents an approach for modeling data collected during emergency response starting from a conceptual level until the implementation in a Database Management System (DBMS). This approach follows arrangements for disaster management in the Netherland. Any disaster incident is managed through processes. Each process has a well-defined objective, which realization requires certain information and often produces information. The information is mostly spatial information, and in a large part of a temporal nature. We have created new data types for the temporal and spatiotemporal data: dynamic counts, moving point, and moving region. The model is implemented in Oracle Spatial. The temporal data types are implemented using the Oracle functionality on nested tables. Visualization of spatiotemporal data is done via views that perform un-nesting of tables, and transform the data to simply spatial types. Spatiotemporal data can be used in analysis for supporting the decision making during disaster incidents.

RDF will often be the metadata model of choice in the Semantic Sensor Web. However, RDF can only represent thematic metadata and needs to be extended if we want to model spatial and temporal information. For this purpose, we develop the data model stRDF and the query language stSPARQL. stRDF is a constraint data model that extends RDF with the ability to represent spatial and temporal data. stSPARQL extends SPARQL for querying stRDF data. In our extension to RDF, we follow the main ideas of constraint databases and represent spatial and temporal objects as quantifier-free formulas in a first-order logic of linear constraints. Thus an important contribution of stRDF is to bring to the RDF world the benefits of constraint databases and constraint-based reasoning so that spatial and temporal data can be represented in RDF using constraints.

Building alteration and redevelopment play a central role in the revitalization of developed cities, where the scarcity of available land limits the construction of new buildings. The adaptive reuse of existing space reflects the underlying socioeconomic dynamics of the city and can be a leading indicator of economic growth and diversification. However, the collective understanding of building alteration patterns is constrained by significant barriers to data accessibility and analysis. We present a data mining and knowledge discovery process for extracting, analyzing, and integrating building permit data for more than 2,500,000 alteration projects from seven major U.S. cities. We utilize natural language processing and topic modeling to discover the thematic structure of construction activities from permit descriptions and merge with other urban data to explore the dynamics of urban change. The knowledge discovery process proceeds in three steps: (1) text mining to identify popular words, popularity change, and their co-appearance likelihood; (2) topic modeling using latent Dirichlet allocation (LDA); and (3) integrating the topic modeling output with building information and ancillary data to discover the spatial, temporal, and thematic patterns of urban redevelopment and regeneration. The results demonstrate a generalizable approach that can be used to analyze unstructured text data extracted from permit records across varying database structures, permit typologies, and local contexts. Our machine learning methodology can assist cities to better monitor building alteration activity, analyze spatiotemporal patterns of redevelopment, and more fully understand the economic, social, and environmental implications of changes to the urban built environment.

Many applications such as planning, scheduling, computational linguistics and computational models for molecular biology involve systems capable of managing qualitative and metric time information. An important issue in designing such systems is the efficient handling of temporal information in an evolutive environment. In a previous work, we have developed a temporal model, TemPro, based on the interval algebra, to express such information in terms of qualitative and quantitative temporal constraints. In order to find a good policy for solving time constraints in a dynamic environment, we present in this paper, a study of dynamic arc-consistency algorithms in the case of temporal constraints. We show that, an adaptation of the new AC-3 algorithm presents promising results comparing to the other dynamic arc-consistency algorithms. Indeed, while keeping an optimal worst-case time complexity, this algorithm has a better space complexity than the other methods.

The ubiquity of mobile location aware devices and the proliferation of social networks have given rise to Location-Aware Social Networks (LASN), where users form social connections and make geo-referenced posts. The goal of this paper is to identify users that can influence a large number of important other users, within a given spatial region. Returning a ranked list of regionally influential LASN users is useful in viral marketing and in other per-region analytical scenarios. We show that under a general influence propagation model, the problem is #P-hard, while it becomes solvable in polynomial time in a more restricted model. Under the more restrictive model, we then show that the problem can be translated to computing a variant of the so-called closeness centrality of users in the social network, and devise an evaluation method.

Many applications such as planning, scheduling, computational linguistics and computational models for molecular biology involve systems capable of managing qualitative and metric time information. An important issue in designing such systems is the efficient handling of temporal information in an evolutive environment. In a previous work, we have developed a temporal model, TemPro, based on the interval algebra, to express such information in terms of qualitative and quantitative temporal constraints. In order to find a good policy for solving time constraints in a dynamic environment, we present in this paper, a study of dynamic arc-consistency algorithms in the case of temporal constraints. We show that, an adaptation of the new AC-3 algorithm presents promising results comparing to the other dynamic arc-consistency algorithms. Indeed, while keeping an optimal worst-case time complexity, this algorithm has a better space complexity than the other methods.