In query-intensive database application areas, like decision support and data mining, systems that use vertical fragmentation have a significant performance advantage. In order to support relational or object oriented applications on top of such a fragmented data model, a flexible yet powerful intermediate language is needed. This problem has been successfully tackled in Monet, a modern extensible database kernel developed by our group. We focus on the design choices made in the Monet Interpreter Language (MIL), its algebraic query language, and outline how its concept of tactical optimization enhances and simplifies the optimization of complex queries. Finally, we summarize the experience gained in Monet by creating a highly efficient implementation of MIL.

Algebraic transformation and optimization techniques have been the method of choice in relational query execution, but applying them in OODBMS has been difficult due to the complexity of object-oriented query languages. This paper demonstrates that the problem can be simplified by mapping a complex storage model to the flat binary model implemented by Monet, a state-of-theart database kernel. We present a generic mapping scheme to flatten data models and study the case of a straightforward object-oriented model. We show how flattening enabled us to implement a full-fledged query algebra on it, using only a very limited set of simple operations. The required primitives and query execution strategies are discussed, and their performance is evaluated on the 1GB TPC-D benchmark, showing that our divide-and-conquer approach yields excellent results. 1 Introduction During the last decade, relational database technology has grown towards industrial maturity, and the attention of the research...

The explosion in complex multi-media content makes it crucial for database systems to support such data efficiently. We make the case that the next generation of object-relational database Data Type (E-ADT) technology, rather than on the “blackbox ” ADTs used in current systems. An E-ADT is an abstract data type that exposes the semantics of its methods. Query optimizations are performed using these semantics, resulting in efficient query processing. The added functionality does not compromise the modularity of data types and the extensibility of the type system. Fundamental architectural changes are required to build such a database system; these have been explored through the implementation of E-ADTs in Predutor. Initial performance results demonstrate an order of magnitude in performance improvements. 1

We consider the class of path-conjunctive queries and constraints (dependencies) defined over complex values with dictionaries.

We present an optimization method and algorithm designed for three objectives: physical data independence, semantic optimization, and generalized tableau minimization. The method relies on generalized forms of chase and "backchase" with constraints (dependencies). By using dictionaries (finite functions) in physical schemas we can capture with constraints useful access structures such as indexes, materialized views, source capabilities, access support relations, gmaps, etc. The search space for query plans is de ned and enumerated in a novel manner: the chase phase rewrites the original query into a "universal" plan that integrates all the access structures and alternative pathways that are allowed by applicable constraints. Then, the backchase phase produces optimal plans by eliminating various combinations of redundancies, again according to constraints. This method is applicable (sound) to a large class of queries, physical access structures, and semantic constraints. We prove that it is in fact complete for "path-conjunctive" queries and views with complex objects, classes and dictionaries, going beyond previous theoretical work on processing queries using materialized views.

Domain-independent planning is a hard combinatorial problem. Taking into account plan quality makes the task even more difficult. This article introduces Planning by Rewriting (PbR), a new paradigm for efficient high-quality domain-independent planning. PbR exploits declarative plan-rewriting rules and efficient local search techniques to transform an easy-to-generate, but possibly suboptimal, initial plan into a high-quality plan. In addition to addressing the issues of planning efficiency and plan quality, this framework offers a new anytime planning algorithm. We have implemented this planner and applied it to several existing domains. The experimental results show that the PbR approach provides significant savings in planning effort while generating high-quality plans.

Abstract | This paper tells the story of OQL, the standard query language of the Object Database Management Group (ODMG) [30]. The story starts in 1988, at INRIA in the Alta r Group y. The objective of that group was to develop an object-oriented database system [41]. This objective was reached: in September 1991 the O2 database system started its commercial career as the main product of a company called O2Technology [6]. As opposed to its competitors, O2 featured a full- edged query language named O2SQL [22]. The story goes on with the creation of the ODMG in 1991 and the adoption of O2SQL as the standard object query language under its new and nal name: OQL. During the following years, OQL went through some modi cations, the most important ofwhich resulted in OQL 1.2 that o ers some level of compliance with SQL92. On top of providing the expressive power of the SQL92 query language [54], OQL allows objects to be queried. This is a claim also supported by the upcoming SQL3. However, due to its adequacy to the object oriented type system and its functional nature, OQL is much simpler to learn, use and implement. A goal of this paper is to demonstrate this. This paper tells about the mistakes and pertinent choices we made while designing and implementing OQL. I hope it also conveys the great pleasure I had to be part of this adventure. Key words: Object-oriented database, query language 1.

The Internet provides access to a wealth of information. For any given topic or application domain there are a variety of available information sources. However, current systems, such as search engines or topic directories in the World Wide Web, offer only very limited capabilities for locating, combining, and organizing information. Mediators, systems that provide integrated access and database-like query capabilities to information distributed over heterogeneous sources, are critical to realize the full potential of meaningful access to networked information. Query planning, the task of generating a cost-efficient plan that computes a user query from the relevant information sources, is central to mediator systems. However, query planning is a computationally hard problem due to the large number of possible sources and possible orderings on the operations to process the data. Moreover, the choice of sources, data processing operations, and their ordering, strongly affects the plan c...

Rule-based optimizers are extensible because they consist of modifiable sets of rules. For modification to be straightforward, rules must be easily reasonedabout (i.e., understood and verified). At the same time, rules must be expressive and efficient (to fire) for rulebased optimizers to be practical. Production-style rules (as in [15]) are expressed with code and are hard to reason about. Pure rewrite rules (as in [1]) lack code, but cannot atomically express complex transformations (e.g., normalizations). Some systems allow rules to be grouped, but sacrifice efficiency by providing limited control over their firing. Therefore, none of these approaches succeeds in making rules expressive, efficient and understandable. We propose a language (COKO) for expressing an alternative form of input to a rule-based optimizer. A COKO transformation consists of a set of declarative (KOLA) rewrite rules and a (firing) algorithm that specifies their firing. It is straightforward to reason about C...

Traditionally, query optimizers assume a direct mapping from the logical entities modeling the data (e.g. relations) and the physical entities storing the data (e.g. indexes), each physical entity corresponding precisely to one logical entity. This assumption is no longer true in non-traditional applications (object-oriented and semi-structured databases, data integration), which often exhibit a mismatch between the logical view and the actual storage of data. In addition, there is an increased amount of redundancy, even at the logical level, that can greatly enhance optimization opportunities, if exploited. To deal with all this, we propose a novel architecture for query optimization, in which physical optimization is leveraged at the level of query rewriting. As a consequence, the other important aspect of query optimization, semantic optimization (that takes advantage of the redundancy at the logical level), can be naturally incorporated. The optimizer can then make global decisions based on both semantic and physical knowledge, leading to plans of higher quality than those obtainable by a traditional two-level approach. The main idea