Towards Streaming Evaluation of Queries with Correlation in Complex Event Processing

Complex event processing (CEP) has gained a lot of attention for evaluating complex patterns over high-throughput data streams. Recently, new algorithms for the evaluation of CEP patterns have emerged with strong guarantees of efficiency, i.e. constant update-time per tuple and constant-delay enumeration. Unfortunately, these techniques are restricted for patterns with local filters, limiting the possibility of using joins for correlating the data of events that are far apart. In this paper, we embark on the search for efficient evaluation algorithms of CEP patterns with joins. We start by formalizing the so-called partition-by operator, a standard operator in data stream management systems to correlate contiguous events on streams. Although this operator is a restricted version of a join query, we show that partition-by (without iteration) is equally expressive as hierarchical queries, the biggest class of full conjunctive queries that can be evaluated with constant update-time and constant-delay enumeration over streams. To evaluate queries with partition-by we introduce an automata model, called chain complex event automata (chain-CEA), an extension of complex event automata that can compare data values by using equalities and disequalities. We show that this model admits determinization and is expressive enough to capture queries with partition-by. More importantly, we provide an algorithm with constant update time and constant delay enumeration for evaluating any query definable by chain-CEA, showing that all CEP queries with partition-by can be evaluated with these strong guarantees of efficiency

World Wide Web Metasearch Clustering Algorithm

As the storage capacity and the processing speed of search engine is growing to keep up with the constant expansion of the World Wide Web, the user is facing an increasing list of results for a given query. A simple query composed of common words sometimes have hundreds even thousands of results making it practically impossible for the user to verify all of them, in order to identify a particular site. Even when the list of results is presented to the user ordered by a rank, most of the time it is not sufficient support to help him identify the most relevant sites for his query. The concept of search result clustering was introduced as a solution to this situation. The process of clustering search results consists of building up thematically homogenous groups from the initial list results provided by classic search tools, and using up characteristics present within the initial results, without any kind of predefined categories.search results, clustering algorithm, World Wide Web search

World Wide Web Metasearch Clustering Algorithm

As the storage capacity and the processing speed of search engine is growing to keep up with the constant expansion of the World Wide Web, the user is facing an increasing list of results for a given query. A simple query composed of common words sometimes have hundreds even thousands of results making it practically impossible for the user to verify all of them, in order to identify a particular site. Even when the list of results is presented to the user ordered by a rank, most of the time it is not sufficient support to help him identify the most relevant sites for his query. The concept of search result clustering was introduced as a solution to this situation. The process of clustering search results consists of building up thematically homogenous groups from the initial list results provided by classic search tools, and using up characteristics present within the initial results, without any kind of predefined categories

Fully dynamic data structure for LCE queries in compressed space

A Longest Common Extension (LCE) query on a text $T$ of length $N$ asks for the length of the longest common prefix of suffixes starting at given two positions. We show that the signature encoding $\mathcal{G}$ of size $w = O(\min(z \log N \log^* M, N))$ [Mehlhorn et al., Algorithmica 17(2):183-198, 1997] of $T$, which can be seen as a compressed representation of $T$, has a capability to support LCE queries in $O(\log N + \log \ell \log^* M)$ time, where $\ell$ is the answer to the query, $z$ is the size of the Lempel-Ziv77 (LZ77) factorization of $T$, and $M \geq 4N$ is an integer that can be handled in constant time under word RAM model. In compressed space, this is the fastest deterministic LCE data structure in many cases. Moreover, $\mathcal{G}$ can be enhanced to support efficient update operations: After processing $\mathcal{G}$ in $O(w f_{\mathcal{A}})$ time, we can insert/delete any (sub)string of length $y$ into/from an arbitrary position of $T$ in $O((y+ \log N\log^* M) f_{\mathcal{A}})$ time, where $f_{\mathcal{A}} = O(\min \{ \frac{\log\log M \log\log w}{\log\log\log M}, \sqrt{\frac{\log w}{\log\log w}} \})$. This yields the first fully dynamic LCE data structure. We also present efficient construction algorithms from various types of inputs: We can construct $\mathcal{G}$ in $O(N f_{\mathcal{A}})$ time from uncompressed string $T$; in $O(n \log\log n \log N \log^* M)$ time from grammar-compressed string $T$ represented by a straight-line program of size $n$; and in $O(z f_{\mathcal{A}} \log N \log^* M)$ time from LZ77-compressed string $T$ with $z$ factors. On top of the above contributions, we show several applications of our data structures which improve previous best known results on grammar-compressed string processing.Comment: arXiv admin note: text overlap with arXiv:1504.0695