12123800
Integration of Proximity Relations under Consistency Constraint
Though the data integration problem of relational databases has been well studied, little work addresses the problem in the context of fuzzy relational databases. The fuzzy relational model relies on the specification of either a similarity relation or a proximity relation for each scalar domain in the fuzzy database. Without the constraint of max-min transitivity, proximity relations can be defined in an intuitive way. This paper states a resolution in integrating multiple proximity relations. First, the closure property of weighted combination of multiple proximity relations is shown. Then, the notion of consistency constraint is defined. It has been proved that the weighted combination of proximity relations under such constrain is still consistent with each of the proximity relations prior to their integration.
11169177
Mining maximal frequent intervals
Many real world data are associated with intervals of time or distance. Mining frequent intervals from such data allows the users to group transactions with similar behavior together. Previous work only focuses on the problem of mining frequent intervals in a discrete domain. This paper first proposes the notion of maximal frequent intervals, which are superior to frequent intervals in many perspectives, and then provides a method for mining maximal frequent intervals in either a discrete domain or a continuous domain. Experimental results indicate that our method outperforms previous method for mining frequent intervals, and improves the runtime by two orders of magnitude for databases with a large number of distinct endpoints.
16556041
Segmented fuzzy checkpointing for main memory databases
'Toscane' can be used as a decorative pot plant that flowers early and abundantly. The plant can be sold in different sizes from approximately 40 cm to 80 cm in height. The plant produces abundant flowers that enlarge in size as the plant becomes bigger. The decorative leaves are glossy, dark green, coriaceous and extremely durable. 'Toscane' produces bright, long-lived and large flowers that are held above the foliage. The heart-shaped flowers maintain their original red color as they mature.
546802
A Low-Cost Checkpointing Technique for Distributed Databases
For distributed databases, checkpointing is used to ensure an efficient way to perform global reconstruction. However, the need for global reconstruction is infrequent. Most current checkpointing approaches for distributed databases are too expensive during run time. Some of them allow the checkpointing process to run in parallel with normal transactions at the cost of more data and resource contention, which in turn causes longer response time for normal transactions. Thus, an efficient way to checkpoint distributed databases is needed to avoid degrading the system performance. This paper presents a low-cost solution, called Loosely Synchronized Local Fuzzy Checkpointing (LSLFC), to these problems. LSLFC supports global reconstruction, and our performance study shows that LSLFC has little overhead during run time.
1778183
Assessing the term independence assumption in blind relevance feedback
When applying blind relevance feedback for ad hoc document retrieval, is it possible to identify, a priori, the set of query terms that will most improve retrieval performance? Can this complex problem be reduced into the simpler one of making independent decisions about the performance effects of each query term? Our experiments suggest that, for the selection of terms for blind relevance feedback, the term independence assumption may be empirically justified.
14212803
Merging Databases Under Constraints
The problem of integrating information from conflicting sources comes up in many current applications, such as cooperative information systems, heterogeneous databases, and multiagent systems. We model this by the operation of merging first-order theories. We propose a formal semantics for this operation and show that it has desirable properties, including abiding by majority rule in case of conflict and syntax independence. We apply our semantics to the special case when the theories to be merged represent relational databases under integrity constraints. We then present a way of merging databases that have different or conflicting schemas caused by problems such as synonyms, homonyms or type conflicts mentioned in the schema integration literature.
6908603
Exploring the limits of single-iteration clarification dialogs
Single-iteration clarification dialogs, as implemented in the TREC HARD track, represent an attempt to introduce interaction into ad hoc retrieval, while preserving the many benefits of large-scale evaluations. Although previous experiments have not conclusively demonstrated performance gains resulting from such interactions, it is unclear whether these findings speak to the nature of clarification dialogs, or simply the limitations of current systems. To probe the limits of such interactions, we employed a human intermediary to formulate clarification questions and exploit user responses. In addition to establishing a plausible upper bound on performance, we were also able to induce an "ontology of clarifications" to characterize human behavior. This ontology, in turn, serves as the input to a regression model that attempts to determine which types of clarification questions are most helpful. Our work can serve to inform the design of interactive systems that initiate user dialogs.
5082006
Mining motifs in massive time series databases
The problem of efficiently locating previously known patterns in a time series database (i.e., query by content) has received much attention and may now largely be regarded as a solved problem. However, from a knowledge discovery viewpoint, a more interesting problem is the enumeration of previously unknown, frequently occurring patterns. We call such patterns "motifs", because of their close analogy to their discrete counterparts in computation biology. An efficient motif discovery algorithm for time series would be useful as a tool for summarizing and visualizing massive time series databases. In addition it could be used as a subroutine in various other data mining tasks, including the discovery of association rules, clustering and classification. In this paper we carefully motivate, then introduce, a nontrivial definition of time series motifs. We propose an efficient algorithm to discover them, and we demonstrate the utility and efficiency of our approach on several real world datasets.
7688101
(Not) Finding Rules in Time Series: A Surprising Result with Implications for Previous and Future Research
Time series data is perhaps the most frequently encountered type of data examined by the data mining community. Clustering is perhaps the most frequently used data mining algorithm, being useful in it’s own right as an exploratory technique, and also as a subroutine in more complex data mining algorithms such as rule discovery, indexing, summarization, anomaly detection, and classification. Given these two facts, it is hardly surprising that time series clustering has attracted much attention. The data to be clustered can be in one of two formats: many individual time series, or a single time series, from which individual time series are extracted with a sliding window. Given the recent explosion of interest in streaming data and online algorithms, the latter case has received much attention. In this work we make a surprising claim. Clustering of streaming time series is completely meaningless. More concretely, clusters extracted from streaming time series are forced to obey a certain constraint that is pathologically unlikely to be satisfied by any dataset, and because of this, the clusters extracted by any clustering algorithm are essentially random. While this constraint can be intuitively demonstrated with a simple illustration and is simple to prove, it has never appeared in the literature. An important implication of our findings is that widely used, time series rule discovery algorithms are producing random results!