Philippe Rigaux

The DEDALE system for complex spatial queries

This paper presents DEDALE, a spatial database system intended to overcome some limitations of current systems by providing an abstract and non-specialized data model and query language for the representation and manipulation of spatial objects. DEDALE relies on a logical model based on linear constraints, which generalizes the constraint database model of [KKR90]. While in the classical constraint model, spatial data is always decomposed into its convex components, in DEDALE holes are allowed to fit the need of practical applications. The logical representation of spatial data although slightly more costly in memory, has the advantage of simplifying the algorithms. DEDALE relies on nested relations, in which all sorts of data (thematic, spatial, etc.) are stored in a uniform fashion. This new data model supports declarative query languages, which allow an intuitive and efficient manipulation of spatial objects. Their formal foundation constitutes a basis for practical query optimization. We describe several evaluation rules tailored for geometric data and give the specification of an optimizer module for spatial queries. Except for the latter module, the system has been fully implemented upon the O2 DBMS, thus proving the effectiveness of a constraint-based approach for the design of spatial database systems.

Mobility Patterns

We present a data model for tracking mobile objects and reporting the result of queries. The model relies on a discrete view of the spatio-temporal space, where the 2D space and the time axis are respectively partitioned in a finite set of user-defined areas and in constant-size intervals. We define a generic query language to retrieve objects that match mobility patterns describing a sequence of moves. We also identify a subset of restrictions to this language in order to express only deterministic queries for which we discuss evaluation techniques to maintain incrementally the result of queries. The model is conceptually simple, efficient, and constitutes a practical and effective solution to the problem of continuously tracking moving objects with sequence queries.

Spatio-temporal data handling with constraints

Most spatial information systems are limited to a fixed dimension (generally 2) which is not extensible. On the other hand, the emerging paradigm of constraint databases allows the representation of data of arbitrary dimension, together with abstract query languages. The complexity of evaluating queries though might be costly if the dimension of the objects is really arbitrary. In this paper, we present a data model, based on linear constraints, dedicated to the representation and manipulation of multidimensional data. In order to preserve a low complexity for query evaluation, we restrict the orthographic dimension of an object O, defined as the dimension of the components O1 ,..., On such that O=O1×...× On. This allows to process queries independently on each component, therefore achieving a satisfying trade-off between design simplicity, expressive power of the query language and efficiency of query evaluation. We illustrate these concepts in the context of spatio-temporal databases where space and time are the natural components. This data model has been implemented in the DEDALE system and a spatio-temporal application, with orthographic dimension 2, is currently running, thus showing the practical relevance of the approach.

Multi-Scale Partitions: Application to Spatial and Statistical Databases

We study the impact of scale on data representation from both the modelling and querying points of view. While our starting point was geographical applications, statistical databases also address this problem of data representation at various levels of abstraction. From these requirements, we propose a model which allows: (i) database querying without exact knowledge of the data abstraction level, (ii) the computation of multiple representations of data, one per abstraction level, and (iii) its application to the computation of statistical summaries. The model has been partially implemented with the DBMS O2 by means of tree-structured domains: we give some examples which illustrate the above features.

A Performance Evaluation of Spatial Join Processing Strategies

We provide an evaluation of query execution plans (QEP) in the case of queries with one or two spatial joins. The QEPs assume R*-tree indexed relations and use a common set of spatial joins algorithms, among which one is a novel extension of a strategy based on an on-the-fly index creation prior to the join with another indexed relation. A common platform is used on which a set of spatial access methods and join algorithms are available. The QEPs are implemented with a general iterator-based spatial query processor, allowing for pipelined QEP execution, thus minimizing memory space required for intermediate results.

SD-Rtree: A Scalable Distributed Rtree

We propose a scalable distributed data structure (SDDS) called SD-Rtree. We intend our structure for point and window queries over possibly large spatial datasets distributed on clusters of interconnected servers. SD-Rtree generalizes the well-known Rtree structure. It uses a distributed balanced binary spatial tree that scales with insertions to potentially any number of storage servers through splits of the overloaded ones. A user/application manipulates the structure from a client node. The client addresses the tree through its image that the splits can make outdated. This may generate addressing errors, solved by the forwarding among the servers. Specific messages towards the clients incrementally correct the outdated images.

Building a constraint-based spatial database system: model, languages, and implementation

This paper presents DEDALE, a spatial database system, which provides an abstract and non-specialized data model and query language for representating and manipulating geometric data in arbitrary dimension. DEDALE relies on a logical model based on linear constraints. The main features of the constraint model are: (1) a uniform representation of all sorts of data, including geometric, spatio-temporal or elevation data and (2) an algebraic query language whose formal foundations constitute a basis for practical query optimization.We show the practical relevance of the approach by describing an implementation which builds on standard technology for data storage, database indexing and on the parsing and optimization of SQL. DEDALE validates the linear constraint model over various applications, proposes a user query language based on SQL which allows to query the database in a purely declarative way, and gives some first steps towards query optimization.We believe that this experience is a fruitful step toward sound and consistent database models which hide the complexity of arbitrary geometric data, while keeping manipulation languages intuitive and efficient.

DEDALE, A Spatial Constraint Database

This paper presents a first prototype of a constraint database for spatial information, dedale. Implemented on top of the O2 DBMS, data is stored in an object-oriented framework, with spatial data represented using linear constraints over a dense domain. The query language is the standard OQL, with special functions for constraint solving and geometric operations.

Spatio-Temporal Data Handling with Constraints

Most spatial information systems are limited to a fixed dimension (generally 2) which is not extensible. On the other hand, the emerging paradigm of constraint databases allows the representation of data of arbitrary dimension, together with abstract query languages. The complexity of evaluating queries though might be costly if the dimension of the objects is really arbitrary. In this paper, we present a data model, based on linear constraints, dedicated to the representation and manipulation of multidimensional data. In order to preserve a low complexity for query evaluation, we restrict the orthographic dimension of an object O, defined as the dimension of the components O1 ,..., On such that O=O1×...× On. This allows to process queries independently on each component, therefore achieving a satisfying trade-off between design simplicity, expressive power of the query language and efficiency of query evaluation. We illustrate these concepts in the context of spatio-temporal databases where space and time are the natural components. This data model has been implemented in the DEDALE system and a spatio-temporal application, with orthographic dimension 2, is currently running, thus showing the practical relevance of the approach.

Personalisation Services for Self E-learning Networks

This paper describes the personalisation services designed for self e-learning networks in the SeLeNe project. These include personalised search results, view definition over learning object metadata, metadata generation for composite learning objects and personalised event and change notification services.