Petko Bakalov

Efficient trajectory joins using symbolic representations

Efficiently and accurately discovering similarities among moving object trajectories is a difficult problem that appears in many spatiotemporal applications. In this paper we consider how to efficiently evaluate trajectory joins, i.e., how to identify all pairs of similar trajectories between two datasets. Our approach represents an object trajectory as a sequence of symbols (i.e., a string). Based on special lower-bounding distances between two strings, we propose a pruning heuristic for reducing the number of trajectory pairs that need to be examined. Furthermore, we present an indexing scheme designed to support efficient evaluation of string similarities in secondary storage. Through a comprehensive experimental evaluation we present the advantages of the proposed techniques.

FlexTrack: a system for querying flexible patterns in trajectory databases

We describe the FlexTrack system for querying trajectories using flexible pattern queries. Such queries are composed of a sequence of simple spatio-temporal predicates, e.g., range and nearest-neighbors, as well as complex motion pattern predicates, e.g., predicates that contain variables and constraints. Users can interactively select spatio-temporal predicates to construct such pattern queries using a hierarchy of regions that partition the spatial domain. Several different query processing algorithms are currently implemented and available in the FlexTrack system.

Continuous Spatiotemporal Trajectory Joins

Given the plethora of GPS and location-based services, que- ries over trajectories have recently received much attention. In this paper we examine trajectory joins over streaming spatiotemporal data. Given a stream of spatiotemporal trajectories created by monitored moving objects, the outcome of a Continuous Spatiotemporal Trajectory Join(CSTJ) query is the set of objects in the stream, which have shown similar behavior over a query-specified time interval, relative to the current timestamp. We propose a novel indexing scheme for streaming spatiotemporal data and develop algorithms for CSTJ evaluation, which utilize the proposed indexing scheme and effectively reduce the computation cost and I/O operations. Finally, we present a thorough experimental evaluation of the proposed indexing structure and algorithms.

Maintaining Connectivity in Dynamic Multimodal Network Models

Network data models are frequently used as a mechanism to describe the connectivity between spatial features in many existing and emerging GIS applications (location- based services, transportation design, navigational systems, etc.). Connectivity information is required for solving a wide range of location-based queries like finding the shortest path, service areas discovery, allocation, and distance matrix computation. Nevertheless, real-life networks are dynamic in nature since spatial features can be periodically modified. Such updates may change the connectivity relations with the other features and connectivity must be reestablished. Existing approaches are not suitable for a dynamic environment, since whenever a feature change occurs, the whole network connectivity has to be reconstructed from scratch. In this paper, we propose an efficient algorithm that incrementally maintains connectivity within a dynamic network. Our solution is based on the existing functionality (tables, joins, sorting algorithms) provided by a standard relational DBMS and has been implemented and tested and will be shipped with an upcoming release of the ESRI ArcGIS product.

Versioning of Network Models in a Multiuser Environment

The standard database mechanisms for concurrency control, which include transactions and locking protocols, do not provide the support needed for updating complex geographic data in a multiuser environment. The preferred method to resolve conflicts in GIS systems is to encapsulate the modifications generated by the end users through the use of multiple versions. Multiuser (or versioned) geographic databases allow users to operate as though they have full access to the entire dataset. Instead of relying upon row locking, versioned databases allow multiple users to simultaneously edit the same row. They implement a model for conflict detection and resolution where the first to commit the change wins by default (though clients can manually intervene and select the latter change as the winner). Network models are frequently used as a mechanism to describe the connectivity information between spatial features in many emerging GIS applications. Supporting networks within the context of a versioned database imposes additional requirements --- the complex network model must retain integrity irrespective of the sequence of simultaneous edits by various clients. In this paper, we review our network model and discuss the enhancements necessary to maintaining topological network integrity in this complex environment. Our solution is based on the notion of dirty areas and dirty objects (i.e., regions or elements that contain edits that have not been reflected in the network connectivity index). The dirty areas and objects are identified and marked during editing of the network feature data. They are then subsequently cleaned as a byproduct of the incremental update of the connectivity network.

A Generic Framework for Continuous Motion Pattern Query Evaluation

We introduce a novel query type defined over streaming moving object data, namely, the continuous motion pattern (CMP) queries. A motion pattern is defined as a sequence of distinct spatial predicates, each attached to a temporal constraint. The spatial predicates can be of various types (range, nearest neighbor, etc.) The temporal constraints are relative to the current time instant and are used to specify the order of the spatial predicates on the time axis. A CMP query is continuously reevaluated over streaming spatiotemporal data, producing the moving objects which satisfy the querys motion pattern. We first introduce an easily maintainable indexing scheme for spatiotemporal streams that facilitates the evaluation of the spatial predicates over their temporal constraints. Using this scheme we propose a generic framework for efficiently answering a wide range of CMP queries. The effectiveness of our algorithms in reducing the query computation cost and I/O operations is revealed through a thorough experimental evaluation.

Time dependent transportation network models

Network data models are frequently used as a mechanism to solve wide range of problems typical for the GIS applications and transportation planning in particular. They do this by modelling the two most important aspects of such systems: the connectivity and the attribution. For a long time the attributes like the travel time, associated with a transportation network model have been considered static. With the advancement of the technology data vendors now have the capability to capture more accurate information about the speeds of streets at different times of the day and provide this data to customers. The network attributes are not static anymore but associated with a particular time instance (e.g time-dependent). In this paper we describe our time dependent network model tailored towards the need of transportation network modelling. Our solution is based on the existing database functionality (tables, joins, sorting algorithms) provided by a standard relational DBMS and has been implemented and tested and currently being shipped as a part of the ESRI ArcGIS 10.1 platform and all subsequent releases.

TS2-tree - an efficient similarity based organization for trajectory data

The increasingly popular GPS technology and the growing amount of trajectory data it generates create the need for developing applications that efficiently store and query trajectories of moving objects. In this paper we introduce TS2 tree, a novel indexing structure for organizing trajectory data based on similarity between trajectories. TS2 tree provides lower and upper bounds on distance between trajectories, based on which we propose a general framework for effectively answering a wide range of similarity-based trajectory queries such as similarity threshold (ST) query and similarity best fit (SBF) query. The multifold reduction in query computation times and the number of I/O operations is demonstrated through an extensive experimental evaluation.

Fast transportation network traversal with hyperedges: (industrial paper)

Network data models are frequently used as a mechanism to solve wide range of problems typical for the GIS applications and transportation planning in particular. Because of their popularity and efficiency those models tend to grow in size and complexity. This growth however creates multiple scalability issues caused by the large number of network elements that have to be examined during the network traversal. In this paper we present an extension of our network model tailored towards improving the performance of hierarchical point to point solve operations. The proposed solution is based on introducing a new network edge type that we term hyperedges. We describe how hyperedges can be specified with a re-interpretation of our existing any-vertex connectivity policy on edges, discusses some modeling issues, and also provide insights of our implementation experience and the impact which those novel network elements have on the solve performance. Our solution is based on the existing database functionality (tables, joins, sorting algorithms) provided by a standard relational DBMS and has been implemented and tested and currently being shipped as a part of the ESRI ArcGIS 10.1 platform and all subsequent releases.

Moving beyond transportation: utility network management

Transportation networks and associated analytic algorithms have been extensively studied in the research domain, with numerous systems in widespread production. An equally important domain that has not been well addressed by the research community are network models used to support the utility domain (e.g., water, wastewater, stormwater, gas, electric, pipeline, and telecommunications). The utility domain places a very different set of requirements on a network model and the associated analytic operations than are found with transportation networks. Existing models (e.g., transportation or social graph focused) suffer from a number of limitations and constraints that limit the ability of utility companies from most effectively modeling their network connected infrastructure. Although many utilities have succeeded in implementing systems on top of simple graph models, the solutions have often involved either having to author considerable amounts of custom application code to go with the model (a very expensive and cumbersome proposition), or modifying the workflows in order to compensate for the graph model limitations. This paper proposes the creation of a new utility-centric graph information model to directly support the modeling of utility infrastructures. It is focused on supporting additional requirements for improved performance and scalability (through optimized data models), efficiency and productivity (using a model that supports underground, inside-plant, etc.), data quality (rule-based system which prevents bad data from being created), real-time data acquisition (support for field-based telemetry), enhanced visualization capabilities (schematic visualizations of data), and integration across a services-centric platform model (e.g., RESTful services, desktop, mobile, and web-based clients).