Approximate queries on string data are important due to the prevalence of such data in databases and various conventions and errors in string data. We present the VSol estimator, a novel technique for estimating the selectivity of approximate string queries. The VSol estimator is based on inverse strings and makes the performance of the selectivity estimator independent of the number of strings. To get inverse strings we decompose all database strings into overlapping substrings of length q (q-grams) and then associate each q-gram with its inverse string: the IDs of all strings that contain the q-gram. We use signatures to compress inverse strings, and clustering to group similar signatures. We study our technique analytically and experimentally. The space complexity of our estimator only depends on the number of neighborhoods in the database and the desired estimation error. The time to estimate the selectivity is independent of the number of database strings and linear with respect to the length of query string. We give a detailed empirical performance evaluation of our solution for synthetic and real-world datasets. We show that VSol is effective for large skewed databases of short strings.

Web archives preserve the history of borndigital content and offer great potential for sociologists, business analysts, and legal experts on intellectual property and compliance issues. Data quality is crucial for these purposes. Ideally, crawlers should gather coherent captures of entire Web sites, but the politeness etiquette and completeness requirement mandate very slow, long-duration crawling while Web sites undergo changes. This paper presents the SHARC framework for assessing the data quality in Web archives and for tuning capturing strategies toward better quality with given resources. We define data quality measures, characterize their properties, and develop a suite of quality-conscious scheduling strategies for archive crawling. Our framework includes single-visit and visit-revisit crawls. Single-visit crawls download every page of a site exactly once in an order that aims to minimize the "blur" in capturing the site. Visit-revisit strategies revisit pages after their initial downloads to check for intermediate changes.The revisiting order aims to maximize the "coherence" of the site capture(number pages that did not change during the capture). The quality notions of blur and coherence are formalized in the paper. Blur is a stochastic notion that reflects the expected number of page changes that a time-travel access to a site capture would accidentally see, instead of the ideal view of a instantaneously captured, "sharp" site. Coherence is a deterministic quality measure that counts the number of unchanged and thus coherently captured pages in a site snapshot. Strategies that aim to either minimize blur or maximize coherence are based on prior knowledge of or predictions for the change rates of individual pages. Our framework includes fairly accurate classifiers for change predictions. All strategies are fully implemented in a testbed and shown to be effective by experiments with both synthetically generated sites and a periodic crawl series for different Web sites.

Web archives preserve the history of Web sites and have high long-term value for media and business analysts. Such archives are maintained by periodically re-crawling entire Web sites of interest. From an archivist's point of view, the ideal case to ensure highest possible data quality of the archive would be to "freeze" the complete contents of an entire Web site during the time span of crawling and capturing the site. Of course, this is practically infeasible. To comply with the politeness specification of a Web site, the crawler needs to pause between subsequent http requests in order to avoid unduly high load on the site's http server. As a consequence, capturing a large Web site may span hours or even days, which increases the risk that contents collected so far are incoherent with the parts that are still to be crawled. This paper introduces a model for identifying coherent sections of an archive and, thus, measuring the data quality in Web archiving. Additionally, we present a crawling strategy that aims to ensure archive coherence by minimizing the diffusion of Web site captures. Preliminary experiments demonstrate the usefulness of the model and the effectiveness of the strategy.

No abstract

No abstract