2034508
High-dimensional similarity joins
Many emerging data mining applications require a similarity join between points in a high-dimensional domain. We present a new algorithm that utilizes a new index structure, called the /spl epsiv/-kdB tree, for fast spatial similarity joins on high-dimensional points. This index structure reduces the number of neighboring leaf nodes that are considered for the join test, as well as the traversal cost of finding appropriate branches in the internal nodes. The storage cost for internal nodes is independent of the number of dimensions. Hence the proposed index structure scales to high-dimensional data. Empirical evaluation, using synthetic and real-life datasets, shows that similarity join using the /spl epsiv/-kdB tree is 2 to an order of magnitude faster than the R/sup +/ tree, with the performance gap increasing with the number of dimensions.
566602
Querying Shapes of Histories
We present a shape de nition language, called SDL, for retrieving objects based on shapes contained in the histories associated with these objects. It is a small, yet powerful, language that allows a rich variety of queries about the shapes found in historical time sequences. An interesting feature of SDL is its ability to perform blurry matching. A \blurry" match is one where the user cares about the overall shape but does not care about speci c details. Another important feature of SDL is its e cient implementability. The SDL operators are designed to be greedy to reduce non-determinism, which in turn substantially reduces the amount of back-tracking in the implementation. We give transformation rules for rewriting an SDL expression into a more e cient form as well as an index structure for speeding up the execution of SDL queries. We used SDL to \mine the mined rules" in a data mining application and found that the capability to query the behavior of the mined rules over a period of time led to the discovery of new and interesting information. Current Address: Politecnico di Milano, Italy. Symbol Description lb ub iv fv up slightly increasing transition .05 .19 anyvalue anyvalue Up highly increasing transition .20 1.0 anyvalue anyvalue down slightly decreasing transition -.19 -.05 anyvalue anyvalue Down highly decreasing transition -1.0 -.19 anyvalue anyvalue appears transition from a zero value to a non-zero value 0 1.0 zero nonzero disappears transition from a non-zero value to a zero value -1.0 0 nonzero zero stable the nal value nearly equal to the initial value -.04 .04 anyvalue anyvalue zero both the initial and nal values are zero 0 0 zero zero Table 1: An Illustrative Alphabet A
13589461
Mining Sequential Patterns: Generalizations and Performance Improvements
The problem of mining sequential patterns was recently introduced in [3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transaction-time, and each transaction is a set of items. The problem is to discover all sequential patterns with a user-specified minimum support, where the support of a pattern is the number of data-sequences that contain the pattern. An example of a sequential pattern is“5% of customers bought ‘Foundation’ and ‘Ringworld’ in one transaction, followed by ‘Second Foundation’ in a later transaction”. We generalize the problem as follows. First, we add time constraints that specify a minimum and/or maximum time period between adjacent elements in a pattern. Second, we relax the restriction that the items in an element of a sequential pattern must come from the same transaction, instead allowing the items to be present in a set of transactions whose transaction-times are within a user-specified time window. Third, given a user-defined taxonomy (is-a hierarchy) on items, we allow sequential patterns to include items across all levels of the taxonomy.
14089552
Modeling multidimensional databases
The authors propose a data model and a few algebraic operations that provide semantic foundation to multidimensional databases. The distinguishing feature of the proposed model is the symmetric treatment not only of all dimensions but also measures. The model provides support for multiple hierarchies along each dimension and support for ad hoc aggregates. The proposed operators are composable, reorderable, and closed in application. These operators are also minimal in the sense that none can be expressed in terms of others nor can any one be dropped without sacrificing functionality. They make possible the declarative specification and optimization of multidimensional database queries that are currently specified operationally. The operators have been designed to be translated to SQL and can be implemented either on top of a relational database system or within a special purpose multidimensional database engine. In effect, they provide an algebraic application programming interface (API) that allows the separation of the front end from the back end. Finally, the proposed model provides a framework in which to study multidimensional databases and opens several new research problems.
1258393
Information sharing across private databases
Literature on information integration across databases tacitly assumes that the data in each database can be revealed to the other databases. However, there is an increasing need for sharing information across autonomous entities in such a way that no information apart from the answer to the query is revealed. We formalize the notion of minimal information sharing across private databases, and develop protocols for intersection, equijoin, intersection size, and equijoin size. We also show how new applications can be built using the proposed protocols.
12242182
Mining sequential patterns
We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empirically evaluate their performance using synthetic data. Two of the proposed algorithms, AprioriSome and AprioriAll, have comparable performance, albeit AprioriSome performs a little better when the minimum number of customers that must support a sequential pattern is low. Scale-up experiments show that both AprioriSome and AprioriAll scale linearly with the number of customer transactions. They also have excellent scale-up properties with respect to the number of transactions per customer and the number of items in a transaction.<<ETX>>
9257184
Mining Process Models from Workflow Logs
Modern enterprises increasingly use the workflow paradigm to prescribe how business processes should be performed. Processes are typically modeled as annotated activity graphs. We present an approach for a system that constructs process models from logs of past, unstructured executions of the given process. The graph so produced conforms to the dependencies and past executions present in the log. By providing models that capture the previous executions of the process, this technique allows easier introduction of a workflow system and evaluation and evolution of existing process models. We also present results from applying the algorithm to synthetic data sets as well as process logs obtained from an IBM Flowmark installation.