In valid-time indeterminacy it is known that an event stored in a database did in fact occur, but it is not known exactly when. In this paper we extend the SQL data model and query language to support valid-time indeterminacy. We represent the occurrence time of an event with a set of possible instants, delimiting when the event might have occurred, and a probability distribution over that set. We also describe query language constructs to retrieve information in the presence of indeterminacy. These constructs enable users to specify their credibility in the underlying data and their plausibility in the relationships among that data. A denotational semantics for SQL’s select statement with optional credibility and plausibility constructs is given. We show that this semantics is reliable, in that it never produces incorrect information, is maximal, in that if it were extended to be more informative, the results may not be reliable, and reduces to the previous semantics when there is no indeterminacy. Although the extended data model and query language provide needed modeling capabilities, these extensions appear initially to carry a significant execution cost. A contribution of this paper is to demonstrate that our approach is useful and practical. An efficient representation of valid-time indeterminacy and efficient query processing algorithms are provided. The cost of

Top-k processing in uncertain databases is semantically and computationally different from traditional top-k processing. The interplay between score and uncertainty makes traditional techniques inapplicable. We introduce new probabilistic formulations for top-k queries. Our formulations are based on “marriage ” of traditional top-k semantics and possible worlds semantics. In the light of these formulations, we construct a framework that encapsulates a state space model and efficient query processing techniques to tackle the challenges of uncertain data settings. We prove that our techniques are optimal in terms of the number of accessed tuples and materialized search states. Our experiments show the efficiency of our techniques under different data distributions with orders of magnitude improvement over naïve materialization of possible worlds. 1

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The precise probability of a compound event (e.g. e1 e2 ; e1 e2) depends upon the known relationships (e.g. independence, mutual exclusion, ignorance of any relationship, etc.) between the primitive events that constitute the compound event. To date, most research on probabilistic logic programming [20, 19, 22, 23, 24] has assumed that we are ignorant of the relationship between primitive events. Likewise, most research in AI (e.g. Bayesian approaches) have assumed that primitive events are independent. In this paper, we propose a hybrid probabilistic logic programming language in which the user can explicitly associate, with any given probabilistic strategy, a conjunction and disjunction operator, and then write programs using these operators. We describe the syntax of hybrid probabilistic programs, and develop a model theory and fixpoint theory for such programs. Last, but not least, we develop three alternative procedures to answer queries, each of which is guaranteed to be sound ...

Systems for On-Line Analytical Processing (OLAP) considerably ease the process of analyzing business data and have become widely used in industry. OLAP systems primarily employ multidimensional data models to structure their data. However, current multidimensional data models fall short in their ability to model the complex data found in some real-world application domains. The paper presents nine requirements to multidimensional data models, each of which is exemplified by a real-world, clinical case study. A survey of the existing models reveals that the requirements not currently met include support for many-to-many relationships between facts and dimensions, built-in support for handling change and time, and support for uncertainty as well as different levels of granularity in the data. The paper defines an extended multidimensional data model, which addresses all nine requirements. Along with the model, we present an associated algebra, and outline how to implement the model using relational databases.

The most acute information management challenges today stem from organizations relying on a large number of diverse, interrelated data sources, but having no means of managing them in a convenient, integrated, or principled fashion. These challenges arise in enterprise and government data management, digital libraries, “smart ” homes and personal information management. We have proposed dataspaces as a data management abstraction for these diverse applications and DataSpace Support Platforms (DSSPs) as systems that should be built to provide the required services over dataspaces. Unlike data integration systems, DSSPs do not require full semantic integration of the sources in order to provide useful services. This paper lays out specific technical challenges to realizing DSSPs and ties them to existing work in our field. We focus on query answering in DSSPs, the DSSP’s ability to introspect on its content, and the use of human attention to enhance the semantic relationships in a dataspace. 1.

The degree to which information sources are pre-processed by Web-based information systems varies greatly. In search engines like Altavista, little pre-processing is done, while in "knowledge integration" systems, complex site-specific "wrappers" are used integrate different information sources into a common database representation. In this paper we describe an intermediate between these two models. In our system, information sources are converted into a highly structured collection of small fragments of text. Databaselike queries to this structured collection of text fragments are approximated using a novel logic called WHIRL, which combines inference in the style of deductive databases with ranked retrieval methods from information retrieval. WHIRL allows queries that integrate information from multiple Web sites, without requiring the extraction and normalization of object identifiers that can be used as keys; instead, operations that in conventional databases require equality tests...

The web contains a large quantity of unstructured information. In many cases, it is possible to heuristically extract structured information, but the resulting databases are "soft": they contain inconsistencies and duplication, and lack unique, consistently-used object identifiers. Examples include large bibliographic databases harvested from raw scientific papers or databases constructed by merging heterogeneous "hard" databases. Here we formally model a soft database as a noisy version of some unknown hard database. We then consider the hardening problem, i.e., the problem of inferring the most likely underlying hard database given a particular soft database. A key feature of our approach is that hardening is global --- many sources of evidence for a given hard fact are taken into account. We formulate hardening as an optimization problem and give a nontrivial nearly linear time algorithm for finding a local optimum. Categories and Subject Descriptors H.4.m [Information Systems]: M...

The detection of duplicate tuples, corresponding to the same real-world entity, is an important task in data integration and cleaning. While many techniques exist to identify such tuples, the merging or elimination of duplicates can be a difficult task that relies on ad-hoc and often manual solutions. We propose a complementary approach that permits declarative query answering over duplicated data, where each duplicate is associated with a probability of being in the clean database. We rewrite queries over a database containing duplicates to return each answer with the probability that the answer is in the clean database. Our rewritten queries are sensitive to the semantics of duplication and help a user understand which query answers are most likely to be present in the clean database. The semantics that we adopt is independent of the way the probabilities are produced, but is able to effectively exploit them during query answering. In the absence of external knowledge that associates each database tuple with a probability, we offer a technique, based on tuple summaries, that automates this task. We experimentally study the performance of our rewritten queries. Our studies show that the rewriting does not introduce a significant overhead in query execution time. This work is done in the context of the ConQuer project at the University of Toronto, which focuses on the efficient management of inconsistent and dirty databases. 1

Efficient processing of top-k queries is a crucial requirement in many interactive environments that involve massive amounts of data. In particular, efficient top-k processing in domains such as the Web, multimedia search and distributed systems has shown a great impact on performance. In this survey, we describe and classify top-k processing techniques in relational databases. We discuss different design dimensions in the current techniques including query models, data access methods, implementation levels, data and query certainty, and supported scoring functions. We show the implications of each dimension on the design of the underlying techniques. We also discuss top-k queries in XML domain, and show their connections to relational approaches.