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.
52849452
Integrating association rule mining with relational database systems: alternatives and implications
Data mining on large data warehouses is becoming increasingly important. In support of this trend, we consider a spectrum of architectural alternatives for coupling mining with database systems. These alternatives include: loose-coupling through a SQL cursor interface; encapsulation of a mining algorithm in a stored procedure; caching the data to a file system on-the-fly and mining; tight-coupling using primarily user-defined functions; and SQL implementations for processing in the DBMS. We comprehensively study the option of expressing the mining algorithm in the form of SQL queries using Association rule mining as a case in point. We consider four options in SQL-92 and six options in SQL enhanced with object-relational extensions (SQL-OR). Our evaluation of the different architectural alternatives shows that from a performance perspective, the Cache-Mine option is superior, although the performance of the SQL-OR option is within a factor of two. Both the Cache-Mine and the SQL-OR approaches incur a higher storage penalty than the loose-coupling approach which performance-wise is a factor of 3 to 4 worse than Cache-Mine. The SQL-92 implementations were too slow to qualify as a competitive option. We also compare these alternatives on the basis of qualitative factors like automatic parallelization, development ease, portability and inter-operability.
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
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.
15564115
A Linear Method for Deviation Detection in Large Databases
We describe the problem of finding deviations in large data bases. Normally, explicit information outside the data, like integrity constraints or predefined patterns, is used for deviation detection. In contrast, we approach the problem from the inside of the data, using the implicit redundancy of the data. 
 
We give a formal description of the problem and present a linear algorithm for detecting deviations. Our solution simulates a mechanism familiar to human beings: after seeing a series of similar data, an element disturbing the series is considered an exception. We also present experimental results from the application of this algorithm on real-life datasets showing its effectiveness.
7027713
Discovery-Driven Exploration of OLAP Data Cubes
Analysts predominantly use OLAP data cubes to identify regions of anomalies that may represent problem areas or new opportunities. The current OLAP systems support hypothesis-driven exploration of data cubes through operations such as drill-down, roll-up, and selection. Using these operations, an analyst navigates unaided through a huge search space looking at large number of values to spot exceptions. We propose a new discovery-driven exploration paradigm that mines the data for such exceptions and summarizes the exceptions at appropriate levels in advance. It then uses these exceptions to lead the analyst to interesting regions of the cube during navigation. We present the statistical foundation underlying our approach. We then discuss the computational issue of finding exceptions in data and making the process efficient on large multidimensional data bases.
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.