Visible to the public Efficient Exploration of Algorithm in Scholarly Big Data Document

TitleEfficient Exploration of Algorithm in Scholarly Big Data Document
Publication TypeConference Paper
Year of Publication2018
AuthorsVanjari, M. S. P., Balsaraf, M. K. P.
Conference Name2018 International Conference on Information , Communication, Engineering and Technology (ICICET)
Keywordsalgorithm database, algorithm indexing, algorithm representations, Algorithms, AlgorithmSeer, Big Data, composability, data mining, document handling, indexing, Internet, learning (artificial intelligence), machine learning algorithms, metadata, Metadata Discovery Problem, Portable document format, Pseudo codes, pubcrawl, Resiliency, Scalability, Scholarly big data, scholarly Big Data document, scholarly digital documents, search engine, search engines, searching algorithms, self-learning, Sentence Extractor, Software algorithms, Steaming, textual content, TFIDF
AbstractAlgorithms are used to develop, analyzing, and applying in the computer field and used for developing new application. It is used for finding solutions to any problems in different condition. It transforms the problems into algorithmic ones on which standard algorithms are applied. Day by day Scholarly Digital documents are increasing. AlgorithmSeer is a search engine used for searching algorithms. The main aim of it provides a large algorithm database. It is used to automatically encountering and take these algorithms in this big collection of documents that enable algorithm indexing, searching, discovery, and analysis. An original set to identify and pull out algorithm representations in a big collection of scholarly documents is proposed, of scale able techniques used by AlgorithmSeer. Along with this, particularly important and relevant textual content can be accessed the platform and highlight portions by anyone with different levels of knowledge. In support of lectures and self-learning, the highlighted documents can be shared with others. But different levels of learners cannot use the highlighted part of text at same understanding level. The problem of guessing new highlights of partially highlighted documents can be solved by us.
Citation Keyvanjari_efficient_2018