State-of-the-art in string similarity search and join

String similarity search and its variants are fundamental problems with many applications in areas such as data integration, data quality, computational linguistics, or bioinformatics. A plethora of methods have been developed over the last decades. Obtaining an overview of the state-of-the-art in this field is difficult, as results are published in various domains without much cross-talk, papers use different data sets and often study subtle variations of the core problems, and the sheer number of proposed methods exceeds the capacity of a single research group. In this paper, we report on the results of the probably largest benchmark ever performed in this field. To overcome the resource bottleneck, we organized the benchmark as an international competition, a workshop at EDBT/ICDT 2013. Various teams from different fields and from all over the world developed or tuned programs for two crisply defined problems. All algorithms were evaluated by an external group on two machines. Altogether, we compared 14 different programs on two string matching problems (k-approximate search and k-approximate join) using data sets of increasing sizes and with different characteristics from two different domains. We compare programs primarily by wall clock time, but also provide results on memory usage, indexing time, batch query effects and scalability in terms of CPU cores. Results were averaged over several runs and confirmed on a second, different hardware platform. A particularly interesting observation is that disciplines can and should learn more from each other, with the three best teams rooting in computational linguistics, databases, and bioinformatics, respectively

Efficient String Edit Similarity Join Algorithm

String similarity join is a basic and essential operation in many applications. In this paper, we investigate the problem of string similarity join with edit distance constraints. A trie-based edit similarity join framework has been proposed recently. The main advantage of existing trie-based algorithms is support for similarity join on short strings. The main problem is when joining long and distant strings. These methods generate and maintain lots of similar prefixes called active nodes which need to be further removed in a subsequent pruning phase. With large edit distance, the number of active nodes becomes quite large. In this paper, we propose a new trie-based join algorithm called PreJoin, which improves upon current trie-based join methods. It efficiently finds all similar string pairs using a novel active-node generation method, which minimizes the number of generated active nodes by applying the pruning heuristics early in the generation process. The performance of PreJoin is scaled in two different ways: First, a dynamic reordering of the trie index is used to accelerate the search for similar string pairs. Second, a partitioning method of string space is used to improve performance on large edit distance thresholds. Experiments show that our approach is highly efficient for processing short as well as long strings, and outperforms the state-of-the-art trie-based join approaches by a factor five

Error-Tolerant Big Data Processing

Real-world data contains various kinds of errors. Before analyzing data, one usually needs to process the raw data. However, traditional data processing based on exactly match often misses lots of valid information. To get high-quality analysis results and fit in the big data era, this thesis studies the error-tolerant big data processing. As most of the data in real world can be represented as a sequence or a set, this thesis utilizes the widely-used sequence-based and set-based similar functions to tolerate errors in data processing and studies the approximate entity extraction, similarity join and similarity search problems. The main contributions of this thesis include: 1. This thesis proposes a unified framework to support approximate entity extraction with both sequence-based and set-based similarity functions simultaneously. The experiments show that the unified framework can improve the state-of-the-art methods by 1 to 2 orders of magnitude. 2. This thesis designs two methods respectively for the sequence and the set similarity joins. For the sequence similarity join, this thesis proposes to evenly partition the sequences to segments. It is guaranteed that two sequences are similar only if one sequence has a subsequence identical to a segment of another sequence. For the set similarity join, this thesis proposes to partition all the sets into segments based on the universe. This thesis further extends the two partition-based methods to support the large-scale data processing framework, Map-Reduce and Spark. The partition-based method won the string similarity join competition held by EDBT and beat the second place by 10 times. 3. This thesis proposes a pivotal prefix filter technique to solve the sequence similarity search problem. This thesis shows that the pivotal prefix filter has stronger pruning power and less filtering cost compared to the state-of-the-art filters.Comment: PhD thesis, Tsinghua University, 201