Objectives and outcomes
Students understand the basic aspects of designing and implementing systems for collecting, indexing
and searching documents. They understand the theoretical foundations of indexing and data searching
and are able to apply them in the implementation of a specific information retrieval system.
Lectures
Text search. Text preprocessing. Boolean search model – inverted index, query processing, skip
pointers, phrase queries. Vector search model – relevance assessment, term frequency, document
frequency, collection frequency, TF-IDF, matrix weight. Probabilistic search models. Phase model and
extended Boolean search model. Structured text search. Web search. Web browsers and web crawling.
Link analysis. SEO – Search Engine Optimisation. Multimedia search- images, sound, video.
Search performance. Relevance. Performance evaluation. Search system improvement – display of
search results, classification, search results clustering, relevance of feedback, global extension of
queries.
Practical classes
Available tools and libraries for indexing and data searching. Searching for relational and unreal
databases. Creating an index for a given data corpus. Implementation of various search techniques over
the created index and comparison of the quality of results. Performance comparison.
Application of search engine improvement techniques. Data scraping and data crawling techniques. Indexing data. Downloaded data search. Implementation of multimedia search.