Yellow Tree: A Distributed Main-memory Spatial Index Structure for Moving Objects

Mobile devices equipped with wireless technologies to communicate and positioning systems to locate objects of interest are common place today, providing the impetus to develop location-aware applications. At the heart of location-aware applications are moving objects or objects that continuously change location over time, such as cars in transportation networks or pedestrians or postal packages. Location-aware applications tend to support the tracking of very large numbers of such moving objects as well as many users that are interested in finding out about the locations of other moving objects. Such location-aware applications rely on support from database management systems to model, store, and query moving object data. The management of moving object data exposes the limitations of traditional (spatial) database management systems as well as their index structures designed to keep track of objects\u27 locations. Spatial index structures that have been designed for geographic objects in the past primarily assume data are foremost of static nature (e.g., land parcels, road networks, or airport locations), thus requiring a limited amount of index structure updates and reorganization over a period of time. While handling moving objects however, there is an incumbent need for continuous reorganization of spatial index structures to remain up to date with constantly and rapidly changing object locations. This research addresses some of the key issues surrounding the efficient database management of moving objects whose location update rate to the database system varies from 1 to 30 minutes. Furthermore, we address the design of a highly scaleable and efficient spatial index structure to support location tracking and querying of large amounts of moving objects. We explore the possible architectural and the data structure level changes that are required to handle large numbers of moving objects. We focus specifically on the index structures that are needed to process spatial range queries and object-based queries on constantly changing moving object data. We argue for the case of main memory spatial index structures that dynamically adapt to continuously changing moving object data and concurrently answer spatial range queries efficiently. A proof-of concept implementation called the yellow tree, which is a distributed main-memory index structure, and a simulated environment to generate moving objects is demonstrated. Using experiments conducted on simulated moving object data, we conclude that a distributed main-memory based spatial index structure is required to handle dynamic location updates and efficiently answer spatial range queries on moving objects. Future work on enhancing the query processing performance of yellow tree is also discussed

Moving Object Trajectories Meta-Model And Spatio-Temporal Queries

In this paper, a general moving object trajectories framework is put forward to allow independent applications processing trajectories data benefit from a high level of interoperability, information sharing as well as an efficient answer for a wide range of complex trajectory queries. Our proposed meta-model is based on ontology and event approach, incorporates existing presentations of trajectory and integrates new patterns like space-time path to describe activities in geographical space-time. We introduce recursive Region of Interest concepts and deal mobile objects trajectories with diverse spatio-temporal sampling protocols and different sensors available that traditional data model alone are incapable for this purpose.Comment: International Journal of Database Management Systems (IJDMS) Vol.4, No.2, April 201

Reference Capabilities for Flexible Memory Management: Extended Version

Verona is a concurrent object-oriented programming language that organises all the objects in a program into a forest of isolated regions. Memory is managed locally for each region, so programmers can control a program's memory use by adjusting objects' partition into regions, and by setting each region's memory management strategy. A thread can only mutate (allocate, deallocate) objects within one active region -- its "window of mutability". Memory management costs are localised to the active region, ensuring overheads can be predicted and controlled. Moving the mutability window between regions is explicit, so code can be executed wherever it is required, yet programs remain in control of memory use. An ownership type system based on reference capabilities enforces region isolation, controlling aliasing within and between regions, yet supporting objects moving between regions and threads. Data accesses never need expensive atomic operations, and are always thread-safe.Comment: 87 pages, 10 figures, 5 listings, 4 tables. Extended version of paper to be published at OOPSLA 202

Challenging Issues of Spatio-Temporal Data Mining

The spatio-temporal database (STDB) has received considerable attention during the past few years, due to the emergence of numerous applications (e.g., flight control systems, weather forecast, mobile computing, etc.) that demand efficient management of moving objects. These applications record objects' geographical locations (sometimes also shapes) at various timestamps and support queries that explore their historical and future (predictive) behaviors. The STDB significantly extends the traditional spatial database, which deals with only stationary data and hence is inapplicable to moving objects, whose dynamic behavior requires re-investigation of numerous topics including data modeling, indexes, and the related query algorithms. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we have presented the challenging issues of spatio-temporal data mining. Keywords: database, data mining, spatial, temporal, spatio-tempora

Data Models for Moving Objects in Road Networks – Implementation and Experiences

Paper deals with the specific LBS scenario – Fleet management (FM) and more specifically with systems for Automatic vehicle location (AVL). Well designed and implemented spatial data model for moving objects is one of the most significant elements of any AVL system. In practical applications the results of the latest scientific research are seldom applied, despite the fact that this area has been developing intensively for more than 20 years. The reasons for this are analysed in the paper. Short analysis of functionality of these systems is presented considering the impact of these functionalities on the implemented data model for moving objects and more specifically their impact on spatio-temporal component of the model. The paper especially reviews the possibility of using road networks as a basis for the representation of moving objects data models and a fact that these models are rarely used in practical applications. A solution overcoming this situation is proposed. The solution assumes transition from the system that is not based on road network to the system that is based on network. There are quite few research papers dealing with OSM data models. Therefore, a significant space in this paper is dedicated to the description of these models since OSM data can be valuable for this type of applications

Management and analysis of mobility data

Mobility data describe the movement of objects in a time interval, such as vehicles, pedestrians, animals. Commonly, mobility data take the form of spatial trajectories, i.e. sequences of timestamped positions in a coordinated space. For example, GPS trajectories are a popular class of spatial trajectories collected outdoors. In this project, we consider a variety of mobility data, including indoor trajectories collected using a positioning infrastructure based on UltraWide Band (UWB). Goal of the project is to investigate on recent Moving Object database solutions for the management of UWB trajectories with focus on recent commercial databasesfor IOT data. In particular, the goal is to analyze a recent Moving Object database called MobilityDB

Maritime data integration and analysis: Recent progress and research challenges

The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems

Towards Intelligent and Generic LBS for Drivers and Mobile Users

In this talk I will focus on Location-Based Services (LBS) for hybrid networks composed of both vehicles and mobile users. The motivation is the interest of studying data management solutions that take into account a generic environment where different types of moving objects share different types of data and possibly using different communication technologies (ad hoc wireless communications forming a pure mobile P2P network, hybrid mobile P2P network with support infrastructure nodes, wide-area communications like 3G, etc.). I will start by summarizing some data management challenges for vehicular networks, related to the exchange of events (efficient and effective content-based data dissemination for push-based data access), query processing (pull-based data access by using query dissemination or mobile agent technology), data item relevance evaluation, management of information about scarce resources (like available parking spaces or charge stations for electric vehicles), semantic data management, automatic knowledge extraction from the data items, multimedia data management, incentives, and trust. Then ..

Collaborative Concealment of Spatio-Temporal Mobile Sequential Patterns

Recent advances in communication and information technology such as the increasing accuracy of GPS technology and the portability of wireless communication devices coat the way for Location Based Services LBS Based on the data collected from the location aware mobile devices data mining techniques are used to meet the quality requirements of expected services The efficient management of moving object databases has gained much interest in recent years due to the development of mobile communication and positioning technologies A typical way of representing moving objects is to use the trajectories Much work has focused on the topics of indexing query processing and data mining of moving object trajectories but little attention has been paid to the preservation of privacy in this setting The major contribution of this paper is to provide privacy to the users of Location Based Services along with capturing interesting user s behavior pattern by broaden the ideas presented in the datamining-literatur