291860
The TSIMMIS Approach to Mediation: Data Models and Languages
TSIMMIS—The Stanford-IBM Manager of Multiple InformationSources—is a system for integrating information. It offers a datamodel and a common query language that are designed to support thecombining of information from many different sources. It also offerstools for generating automatically the components that are needed tobuild systems for integrating information. In this paper we shalldiscuss the principal architectural features and their rationale.
206636218
A Comparison between Deductive and Object-Oriented Database Systems
The author will not pretend to be unbiased in the matter of comparing the deductive and object-oriented approaches to new database systems; he believes that despite some important concepts originating with the object-oriented approach, the deductive family of systems will ultimately dominate. We shall explore some of the reasons why the two approaches do not mix well. Fortunately, most of the features of object-oriented database systems can be incorporated in deductive systems (but not vice-versa). Then we argue that declarativeness is important for at least some of the new applications, and that declarative languages cannot be object-oriented in a nontrivial way. We next contrast the approaches with regard to the way they classify data elements. We close with an examination of the prospects for “classless” data, which is motivated by the prospect of databases where the number of distinct classes would be too large to name systematically. There are a number of challenges to implementing such facilities in either paradigm, and we propose some appropriate research questions.
636164
Querying Semistructured Heterogeneous Information
Semistructured data has no absolute schema fixed in advance and its structure may be irregular or incomplete. Such data commonly arises in sources that do not impose a rigid structure (such as the World-Wide Web) and when data is combined from several heterogeneous sources. Data models and query languages designed for well structured data are inappropriate in such environments. Starting with a “lightweight” object model adopted for the TSIMMIS project at Stanford, in this paper we describe a query language and object repository designed specifically for semistructured data. Our language provides meaningful query results in cases where conventional models and languages do not: when some data is absent, when data does not have regular structure, when similar concepts are represented using different types, when heterogeneous sets are present, and when object structure is not fully known. This paper motivates the key concepts behind our approach, describes the language through a series of examples (a complete semantics is available in an accompanying technical report [QRS+94]), and describes the basic architecture and query processing strategy of the “lightweight” object repository we have developed.
209399936
Answering Queries using Templates with Binding Patterns
When integrating heterogeneous information resources, it is often the case that the source is rather limited in the kinds of queries it can answer. If a query is asked of the entire system, we have a new kind of optimization problem, in which we must try to express the given query in terms of the limited query templates that this source can answer. For the case of conjunctive queries, we show how to decide with a nondeterministic polynomial-time algorithm whether the given query can be answered. We then extend our results to allow arithmetic comparisons in the given query and in the templates.
2826488
Computing Iceberg Queries Efficiently
Many applications compute aggregate functions (such as COUNT, SUM) over an attribute (or set of attributes) to find aggregate values above some specified threshold. We call such queries iceberg queries because the number of above-threshold results is often very small (the tip of an iceberg), relative to the large amount of input data (the iceberg). Such iceberg queries are common in many applications, including data warehousing, information-retrieval, market basket analysis in data mining, clustering and copy detection. We propose efficient algorithms to evaluate iceberg queries using very little memory and significantly fewer passes over data, as compared to current techniques that use sorting or hashing. We present an experimental case study using over three gigabytes of Web data to illustrate the savings obtained by our algorithms.
2204050
Scalable Techniques for Mining Causal Structures
Mining for association rules in market basket data has proved a fruitful area of research. Measures such as conditional probability (confidence) and correlation have been used to infer rules of the form “the existence of item A implies the existence of item B.” However, such rules indicate only a statistical relationship between A and B. They do not specify the nature of the relationship: whether the presence of A causes the presence of B, or the converse, or some other attribute or phenomenon causes both to appear together. In applications, knowing such causal relationships is extremely useful for enhancing understanding and effecting change. While distinguishing causality from correlation is a truly difficult problem, recent work in statistics and Bayesian learning provide some avenues of attack. In these fields, the goal has generally been to learn complete causal models, which are essentially impossible to learn in large-scale data mining applications with a large number of variables.In this paper, we consider the problem of determining casual relationships, instead of mere associations, when mining market basket data. We identify some problems with the direct application of Bayesian learning ideas to mining large databases, concerning both the scalability of algorithms and the appropriateness of the statistical techniques, and introduce some initial ideas for dealing with these problems. We present experimental results from applying our algorithms on several large, real-world data sets. The results indicate that the approach proposed here is both computationally feasible and successful in identifying interesting causal structures. An interesting outcome is that it is perhaps easier to infer the lack of causality than to infer causality, information that is useful in preventing erroneous decision making.
22691930
Time and Tape Complexity of Pushdown Automaton Languages
An algorithm is presented which will determine whether any string w in Σ*, of length n, is contained in a language L ⊆ Σ* defined by a two-way nondeterministic pushdown automation. This algorithm requires time n3 when implemented on a random access computer. It requires n4 time and n2 tape when implemented on a multitape Turing machine. If the pushdown automaton is deterministic, the algorithm requires n2 time on a random access computer and n2 log n time on a multitape Turing machine.