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teaching:projh402 [2015/06/26 16:09] svsummer |
teaching:projh402 [2015/08/18 11:22] svsummer [Project proposals] |
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| ===== Project proposals ===== | ===== Project proposals ===== | ||
| + | === Fast loading of semantic web datasets into native RDF stores ==== | ||
| + | |||
| + | The next generation of the Web, the so-called Semantic Web, stores | ||
| + | extremely large knowledge bases in the RDF data model. In this data | ||
| + | model, all knowledge is represented by means of triples of the form | ||
| + | (subject, property, object), where subject, property and object can be | ||
| + | URLs, among other things. | ||
| + | |||
| + | In order to effeciently query such knowledge bases, the RDF data is | ||
| + | typically loaded into a so-called native RDF store. To ensure that the | ||
| + | knowledge is encoded for fast retrieval, the RDF store will first | ||
| + | encode all variable-length URLs in the dataset by fixed-width | ||
| + | integers, among other things. Each RDF triple will then be encoded by | ||
| + | by their corresponding integer triples (integer_of_subject, | ||
| + | integer_of_property, integer_of_object). | ||
| + | |||
| + | The purpose of this project is to implement and experementally compare | ||
| + | a number of algorithms that can perform this encoding: | ||
| + | |||
| + | * The trivial alogrithm that simply maintains a hashmap that maps URLs to their integer codes. When processing triple (s,p,o), it looks up s, p, and o in the hashmap to see if they have alraedy been assigned an integer ID. If so, this id is used for the encoding; otherwise they are inserted into the hashmap with new, unique ids. The downside of this approach is that, while simple, it requires that one can store all URLS in working memory. | ||
| + | |||
| + | * The slightly smarter algorithm that works in multiple stages: the ID is computed by a pre-fixed hash function. For each URL, the URL and its ID are written to an output file. This file is later sorted on ID to check for possible hash collisions between distinct URLS. | ||
| + | |||
| + | * Algorithms that use the best known state-of-the art data structures for compactly storing respresenting sets of strings, such as the HAT-TRIE ("Engineering scalable, cache and space efficient tries for strings" Nikolas Askitis, Ranjan Sinha, The VLDB Journal, October 2010, Volume 19, Issue 5, pp 633-660 and "HAT-Trie: A Cache-Conscious Trie-Based Data Structure For Strings", The 30th International Australasian Computer Science Conference (ACSC), Volume 62, pages 97 - 105, 2007.). | ||
| + | |||
| + | * Variations of the above algorithms, fine-tuned for semantic web datasets. | ||
| + | |||
| + | |||
| + | **Contact** : Stijn Vansummeren (stijn.vansummeren@ulb.ac.be) | ||
| + | |||
| + | **Status**: available | ||
| ===== Graph Indexing for Fast Subgraph Isomorphism Testing ===== | ===== Graph Indexing for Fast Subgraph Isomorphism Testing ===== | ||