Indexing and querying segmented web pages: the BlockWeb Model

Bruno, Emmanuel; Faessel, Nicolas; Glotin, Hervé; Maitre, Jacques Le; Scholl, Micheł

doi:10.1007/s11280-011-0124-6

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…This identification is dependent on a palette of features: HTML heuristics [19,30], visual clues [6,26,37], or popular "shallow" text features (e.g., link density or text length). Content features may be fed to a machine learning algorithm, for either clustering [3,4,36], or classification [5,33].…”

Section: Related Workmentioning

confidence: 99%

Forest

Oita

Senellart

2015

Proceedings of the 18th International Workshop on Web and Databases

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Content-intensive websites, e.g., of blogs or news, present pages that contain Web articles automatically generated by content management systems. Identification and extraction of their main content is critical in many applications, such as indexing or classification. We present a novel unsupervised approach for the extraction of Web articles from dynamically-generated Web pages. Our system, called FOREST, combines structural and information-based features to target the main content generated by a Web source, and published in associated Web pages. We extensively evaluate FOREST with respect to various baselines and datasets, and report improved results over state-of-the art techniques in content extraction.

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Section: Related Workmentioning

confidence: 99%

Forest

Oita

Senellart

2015

Proceedings of the 18th International Workshop on Web and Databases

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“…In this sense, our partition-based indexing is complementary to the query optimization techniques. Partitioning of blocks is natural and can be applied in Web search as well [9]. In particular, to index and query with partitions, Web pages are segmented into several blocks according to their contents, such as titles, articles, images, etc.…”

Section: Related Workmentioning

confidence: 99%

Indexing dataspaces with partitions

Song

Chen

2012

World Wide Web

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Dataspaces are recently proposed to manage heterogeneous data, with features like partially unstructured, high dimension and extremely sparse. The inverted index has been previously extended to retrieve dataspaces. In order to achieve more efficient access to dataspaces, in this paper, we first introduce our survey of data features in the real dataspaces. Based on the features observed in our study, several partitioning based index approaches are proposed to accelerate the query processing in dataspaces. Specifically, the vertical partitioning index utilizes the partitions on tokens to merge and compress data. We can both reduce the number of I/O reads and avoid aggregation of data inside a compressed list. The horizontal partitioning index supports pruning partitions of tuples in the top-k query. Thus, we can reduce the computation overhead of irrelevant candidate tuples to the query. Finally, we also propose a hybrid index with both vertical and horizontal partitioning. The extensive experiment results in real data sets demonstrate that our approaches outperform the previous techniques and scale well with the large data size.

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