Alessandra Raffaetà

Building real-world trajectory warehouses

The flow of data generated from low-cost modern sensing technologies and wireless telecommunication devices enables novel research fields related to the management of this new kind of data and the implementation of appropriate analytics for knowledge extraction. In this work, we investigate how the traditional data cube model is adapted to trajectory warehouses in order to transform raw location data into valuable information. In particular, we focus our research on three issues that are critical to trajectory data warehousing: (a) the trajectory reconstruction procedure that takes place when loading a moving object database with sampled location data originated e.g. from GPS recordings, (b) the ETL procedure that feeds a trajectory data warehouse, and (c) the aggregation of cube measures for OLAP purposes. We provide design solutions for all these issues and we test their applicability and efficiency in real world settings.

Spatio-temporal aggregations in trajectory data warehouses

In this paper we investigate some issues related to the design of a simple Data Warehouse (DW), storing several aggregate measures about trajectories of moving objects. First we discuss the loading phase of our DW which has to deal with overwhelming streams of trajectory observations, possibly produced at different rates, and arriving in an unpredictable and unbounded way. Then, we focus on the measure presence, the most complex measure stored in our DW. Such a measure returns the number of trajectories that lie in a spatial region during a given temporal interval. We devise a novel way to compute an approximate, but very accurate, presence aggregate function, which algebraically combines a bounded amount of measures stored in the base cells of the data cube.

A general framework for trajectory data warehousing and visual OLAP

In this paper we present a formal framework for modelling a trajectory data warehouse (TDW), namely a data warehouse aimed at storing aggregate information on trajectories of moving objects, which also offers visual OLAP operations for data analysis. The data warehouse model includes both temporal and spatial dimensions, and it is flexible and general enough to deal with objects that are either completely free or constrained in their movements (e.g., they move along a road network). In particular, the spatial dimension and the associated concept hierarchy reflect the structure of the environment in which the objects travel. Moreover, we cope with some issues related to the efficient computation of aggregate measures, as needed for implementing roll-up operations. The TDW and its visual interface allow one to investigate the behaviour of objects inside a given area as well as the movements of objects between areas in the same neighbourhood. A user can easily navigate the aggregate measures obtained from OLAP queries at different granularities, and get overall views in time and in space of the measures, as well as a focused view on specific measures, spatial areas, or temporal intervals. We discuss two application scenarios of our TDW, namely road traffic and vessel movement analysis, for which we built prototype systems. They mainly differ in the kind of information available for the moving objects under observation and their movement constraints.

T-Warehouse: Visual OLAP analysis on trajectory data

Technological advances in sensing technologies and wireless telecommunication devices enable novel research fields related to the management of trajectory data. As it usually happens in the data management world, the challenge after storing the data is the implementation of appropriate analytics for extracting useful knowledge. However, traditional data warehousing systems and techniques were not designed for analyzing trajectory data. Thus, in this work, we demonstrate a framework that transforms the traditional data cube model into a trajectory warehouse. As a proof-of-concept, we implemented T-WAREHOUSE, a system that incorporates all the required steps for Visual Trajectory Data Warehousing, from trajectory reconstruction and ETL processing to Visual OLAP analysis on mobility data.

Spatio-temporal Annotated Constraint Logic Programming

We extend Temporal Annotated Constraint Logic Programming (TACLP) in order to obtain a framework where both temporal and spatial information can be dealt with and reasoned about. This results in a conceptually simple, uniform setting, called STACLP (Spatio-Temporal Annotated Constraint Logic Programming), where temporal and spatial data are represented by means of annotations that label atomic first-order formulae. The expressiveness and conciseness of the approach are illustrated by means of some examples: Definite, periodic and indefinite spatio-temporal information involving time-varying objects and properties can be handled in a natural way.

Active-U-Datalog: Integrating Active Rules in a Logical Update Language

Deductive database technology represents an important step towards the goal of developing highly-declarative database programming languages. In order to make deductive databases a practical technology, deductive rules have to be extended to provide a dynamic behavior. In particular, current applications require not only a support for updates and transactions but also the ability to automatically react to the occurrence of particular events. This is possible by integrating typical deductive rules, whose execution is user-dependent, with active rules, whose execution is event-dependent. Current solutions to this problem are not completely satisfactory. In particular, they often lack a clear semantics, guaranteeing termination, confluence and efficient evaluation. The aim of this paper is to propose a new language for integrating active rules, deductive rules and updates in a uniform logical context. The language we propose is based on the U-Datalog language [9], and extends it with support for active rules, modeled according to the PARK semantics [23]. The resulting language allows the representation of several dynamic aspects, such as transaction execution, reactive behavior and update propagation, in a uniform logical framework, admitting a clear and flexible semantics.

Frequent spatio-temporal patterns in trajectory data warehouses

In this paper we present an approach for storing and aggregating spatio-temporal patterns by using a Trajectory Data Warehouse (TDW). In particular, our aim is to allow the analysts to quickly evaluate frequent patterns mined from trajectories of moving objects occurring in a specific spatial zone and during a given temporal interval. We resort to a TDW, based on a data cube model, having spatial and temporal dimensions, discretized according to a hierarchy of regular grids, and whose facts are sets of trajectories which intersect the spatio-temporal cells of the cube. The idea is to enrich such a TDW with a new measure: frequent patterns obtained from a data-mining process on trajectories. As a consequence these patterns can be analysed by the user at various levels of granularity by means of OLAP queries. The research issues discussed in this paper are (1) the extraction/mining of the patterns to be stored in each cell, which requires an adequate projection phase of trajectories before mining; (2) the spatio-temporal aggregation of patterns to answer roll-up queries, which poses many problems due to the holistic nature of the aggregation function.

Mob-Warehouse: A Semantic Approach for Mobility Analysis with a Trajectory Data Warehouse

The effective analysis and understanding of huge amount of mobility data have been a hot research topic in the last few years. In this paper, we introduce Mob-Warehouse, a Trajectory Data Warehouse which goes a step further to the state of the art on mobility analysis since it models trajectories enriched with semantics. The unit of movement is the (spatio-temporal) point endowed with several semantic dimensions including the activity, the transportation means and the mobility patterns. This model allows us to answer the classical Why, Who, When, Where, What, How questions providing an aggregated view of different aspects of the user movements, no longer limited to space and time. We briefly present an experiment of Mob-Warehouse on a real dataset.

Integrating knowledge representation and reasoning in Geographical Information Systems

We propose a formalism and a programming environment in which sophisticated spatio-temporal reasoning can be performed, while keeping the capabilities of manipulating and presenting large amounts of geographical data, typical of commercial Geographical Information Systems (GISs). The spatio-temporal knowledge representation language, named MuTACLP+, is based on constraint logic programming and is integrated via a middleware of commands and translation features with a commercial GIS. The paper presents the language, the architecture of the environment, and a few examples of its use in the field of event planning.

Enhancing GISs for spatio-temporal reasoning

We present a system which provides geographical information systems (GISs) with enhanced capabilities for supporting spatio-temporal reasoning. On top of a commercial GIS we build a software layer supplying the user with a declarative spatio-temporal interaction with the underlying GIS. Declarative spatio-temporal reasoning is supported by the language MuTACLP, a constraint logic based knowledge representation language that offers facilities for modeling and handling spatio-temporal information, enriched with some basic operators for combining different spatio-temporal knowledge bases. We describe the architecture of the system and we illustrate an actual implementation. The underlying GIS is ArcGIS 8.1, the language MuTACLP is implemented in Sicstus Prolog and the GIS interface establishing the connection between ArcGIS 8.1 and MuTACLP is realized in Visual Basic. Finally, we highlight the usefulness of this approach by modeling a case study regarding the behavioral ecology of crested porcupines.