![]() Besides scale there is novelty in the fact that these technologies come together at the same time. Specifically, in the web context a focus is seen on large semantically based datasets such as Freebase and on the extraction of high-quality data from the web. Individual and combinations of old technologies being applied in the Big Data context. , have proven to be the most successful applied thus far although scalability issues remain to be addressed.įull size table Old Technologies Applied in a New Context , especially those related to Linked Data csv) or differing data schemas or differing meanings attached to the same syntactic forms (e.g. may take the form of differing syntactic formats (e.g. ![]() this challenge has resulted in the emergence of the areas of stream data processing, stream reasoning, and stream data mining to cope with high volumes of incoming raw data. Large-scale reasoning, semantic processing, data mining, machine learning, and information extraction are required. places scalability at the centre of all processing. What is new however are the challenges raised by the specific characteristics of big data related to the three Vs: The analysis found that the following generic techniques are either useful today or will be in the short to medium term: reasoning (including stream reasoning), semantic processing, data mining, machine learning, information extraction, and data discovery.
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