Managing intervals efficiently in object-relational databases

Lecture Notes in Computer Science, 2001

Intervals represent a fundamental data type for temporal, scientific, and spatial databases where time stamps and point data are extended to time spans and range data, respectively. For OLTP and OLAP applications on large amounts of data, not only intersection queries have to be processed efficiently but also general interval relationships including before, meets, overlaps, starts, finishes, contains, equals, during, startedBy, finishedBy, overlappedBy, metBy, and after. Our new algorithms use the Relational Interval Tree, a purely SQL-based and objectrelationally wrapped index structure. The technique therefore preserves the industrial strength of the underlying RDBMS including stability, transactions, and performance. The efficiency of our approach is demonstrated by an experimental evaluation on a real weblog data set containing one million sessions.

2005

With the increasing occurrence of temporal and spatial data in present-day database applications, the interval data type is adopted by more and more database systems. For an efficient support of queries that contain selections on interval attributes as well as simple-valued attributes (e. g. numbers, strings) at the same time, special index structures are required supporting both types of predicates in combination. Based on the Relational Interval Tree, we present various indexing schemes that support such combined queries and can be integrated in relational database systems with minimum effort. Experiments on different query types show superior performance for the new techniques in comparison to competing access methods.

Proceedings International Database Engineering and Applications Symposium

Advanced data warehouses and web databases have set the demand for processing large sets of time ranges, quality classes, fuzzy data, personalized data and extended objects. Since, all of these data types can be mapped to intervals, interval indexing can dramatically speed up or even be an enabling technology for these new applications. We introduce a method for managing intervals by indexing the dual space with the UB-Tree. We show that our method is an effective and efficient solution, benefitting from all good characteristics of the UB-Tree, i.e., concurrency control, worst case guarantees for insertion, deletion and update as well as efficient query processing. Our technique can easily be integrated into an RDBMS engine providing the UB-Tree as access method. We also show that our technique is superior and more flexible to previously suggested techniques.

Lecture Notes in Computer Science, 2004

The efficient management of interval sequences represents a core requirement for many temporal and spatial database applications. With the Relational Interval Tree (RI-tree), an efficient access method has been proposed to process intersection queries of spatial objects encoded by interval sequences on top of existing object-relational database systems. This paper complements that approach by effective and efficient models to estimate the selectivity and the I/O cost of interval sequence intersection queries in order to guide the cost-based optimizer whether and how to include the RI-tree into the execution plan. By design, the models immediately fit to common extensible indexing/optimization frameworks, and their implementations exploit the built-in statistics facilities of the database server. According to our experimental evaluation on an Oracle database, the average relative error of the estimated query results and costs lies in the range of 0% to 32%, depending on the size and the structural complexity of the query objects.

Computer Science and Information Systems, 2010

The need for efficient access and management of time dependent data in modern database applications is well recognised and researched. Existing access methods are mostly derived from the family of spatial R-tree indexing techniques. These techniques are particularly not suitable to handle data involving open ended intervals, which are common in temporal databases. This is due to overlapping between nodes and huge dead space found in the database. In this study, we describe a detailed investigation of a new approach called "Triangular Decomposition Tree" (TD-Tree). The underlying idea for the TD-Tree is to manage temporal intervals by virtual index structures relying on geometric interpretations of intervals, and a space partition method that results in an unbalanced binary tree. We demonstrate that the unbalanced binary tree can be efficiently manipulated using a virtual index. We also show that the single query algorithm can be applied uniformly to different query types without the need of dedicated query transformations. In addition to the advantages related to the usage of a single query algorithm for different query types and better space complexity, the empirical performance of the TDtree has been found to be superior to its best known competitors.

The VLDB Journal

The interval join is a popular operation in temporal, spatial, and uncertain databases. The majority of interval join algorithms assume that input data reside on disk and so, their focus is to minimize the I/O accesses. Recently, an in-memory approach based on plane sweep (PS) for modern hardware was proposed which greatly outperforms previous work. However, this approach relies on a complex data structure and its parallelization has not been adequately studied. In this article, we investigate in-memory interval joins in two directions. First, we explore the applicability of a largely ignored forward scan (FS)-based plane sweep algorithm, for single-threaded join evaluation. We propose four optimizations for FS that greatly reduce its cost, making it competitive or even faster than the state-of-the-art. Second, we study in depth the parallel computation of interval joins. We design a non-partitioning-based approach that determines independent tasks of the join algorithm to run in pa...

Reliable Computing, 1996

Lecture Notes in Computer Science, 1998

To support temporal operators and to increase the efficiency of temporal queries, indexing based on temporal attributes is required. We consider the problem of indexing the temporal dimension in valid time databases. We assume that the temporal information of data objects are represented as valid time intervals that have to be managed dynamically by an efficient index structure. Unlike the time intervals in transaction time databases, valid time intervals can be inserted, deleted, and modified at any point in time. Furthermore, their lifespans can go beyond the current time point and extend into the future. We propose an indexing scheme that uses augmented B+trees called Interval B+trees for indexing a dynamic set of valid time intervals. Interval B+trees (IB+trees) use beginning points of the intervals as key points and keep maximum end point information of its subtrees for each internal node. We introduce an algorithm to apply time-splits at the leaf level of the IB+tree that would partition long valid time intervals into disjoint subintervals and distribute them among several leaf nodes to increase efficiency of search operation, especially for timeslice queries. We compared IB+trees with time-splits to one dimensional R-trees and observed that while their performances for timeslice queries are comparable, IB+trees are far more superior for many temporal queries that are based on beginning points of time intervals. This is expected as the IB+trees use the beginning points of intervals as keys and therefore support such queries naturally. We also show the extensions to our indexing scheme for handling open ended valid time intervals (valid time intervals whose lifespans extend into future indefinitely), and valid time intervals whose end points move along the current timeline.

Lecture Notes in Computer Science, 1994

We identify a number of probicms concerning the management of interval data and propose efficient algorithms in the case of 2dimensional interval relations. The approach is of practical importance and has many applications, one of which is spatiotemporal databases.

IEEE Transactions on Knowledge and Data Engineering, 1997

IXSQL, an extension to SQL, is proposed for the management of interval data. IXSQL is syntactically and semantically upwards consistent with SQL2. Its specification has been based both on theoretical results and actual user requirements for the management of temporal data, a special case of interval data. Design decisions and implementation issues are also discussed.