Duplicate detection is the task of identifying all groups of records within a data set that represent the same real-world entity, respectively. This task is difficult, because (i) representations might differ slightly, so some similarity measure must be defined to compare pairs of records and (ii) data sets might have a high volume making a pair-wise comparison of all records infeasible. To tackle the second problem, many algorithms have been suggested that partition the data set and compare all record pairs only within each partition. One well-known such approach is the Sorted Neighborhood Method (SNM), which sorts the data according to some key and then advances a window over the data comparing only records that appear within the same window. We propose with the Duplicate Count Strategy (DCS) a variation of SNM that uses a varying window size. It is based on the intuition that there might be regions of high similarity suggesting a larger window size and regions of lower similarity suggesting a smaller window size. Next to the basic variant of DCS, we also propose and thoroughly evaluate a variant called DCS++ which is provably better than the original SNM in terms of efficiency (same results with fewer comparisons).

Duplicate detection determines different representations of real-world objects in a database. Recent research has considered the use of relationships among object representations to improve duplicate detection. In the general case where relationships form a graph, research has mainly focused on duplicate detection quality/effectiveness. Scalability has been neglected so far, even though it is crucial for large real-world duplicate detection tasks. We scale-up duplicate detection in graph data (DDG) to large amounts of data and pairwise comparisons, using the support of a relational database management system. To this end, we first present a framework that generalizes the DDG process. We then present algorithms to scale DDG in space (amount of data processed with bounded main memory) and in time. Finally, we extend our framework to allow batched and parallel DDG, thus further improving efficiency. Experiments on data of up to two orders of magnitude larger than data considered so far in DDG show that our methods achieve the goal of scaling DDG to large volumes of data.

The goal of data fusion is to combine several representations of one real world object into a single, consistent representation, e.g., in data integration. A very popular operator to perform data fusion is the minimum union operator. It is defined as the outer union and the subsequent removal of subsumed tuples. Minimum union is used in other applications as well, for instance in database query optimization to rewrite outer join queries, in the semantic web community in implementing SPARQL’s OPTIONAL operator, etc. Despite its wide applicability, there are only few efficient implementations, and until now, minimum union is not a relational database primitive. This paper fills this gap as we present implementations of subsumption that serve as a building block for minimum union. Furthermore, we consider this operator as database primitive and show how to perform optimization of query plans in presence of subsumption and minimum union through rule-based plan transformations. Experiments on both artificial and real world data show that our algorithms outperform existing algorithms used for subsumption in terms of runtime and they scale to large volumes of data. In the context of data integration, we observe that performing data fusion calls for more than subsumption and minimum union. Therefore, another contribution of this paper is the definition of the complementation and complement union operators. Intuitively, these allow to merge tuples that have complementing values and thus eliminate unnecessary null-values. Research was partially performed while at Hasso-Plattner-Institut. Research was partially performed while at Hasso-Plattner-Institut