Miroslav Galabov

St. Cyril and St. Methodius University of Veliko Tarnovo

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Tsvetanka Georgieva‐Trifonova

St. Cyril and St. Methodius University of Veliko Tarnovo

Teodor Kalushkov

St. Cyril and St. Methodius University of Veliko Tarnovo

Donika Valcheva

St. Cyril and St. Methodius University of Veliko Tarnovo

Martin Djonov

St. Cyril and St. Methodius University of Veliko Tarnovo

Miroslav Dechev

Bulgarian Academy of Sciences

Yulian Pastarmov

Google (Switzerland)

Cooperative Institutions

St. Cyril and St. Methodius University of Veliko Tarnovo

Google (Switzerland)

German Research Centre for Artificial Intelligence

Comprehensive Oncological Center – Veliko Turnovo

Bulgarian Academy of Sciences

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Listings for "Transforming 3D Models to Semantic Web Representation"

Harvard Dataverse (2023)

Tsvetanka Georgieva‐Trifonova Miroslav Galabov

For a given Element of the X3D scheme, the necessary RML (RDF Mapping Language) rules can be systematized as follows: A. Rules that generate statements about the resource belonging to a certain class (Listing 1); B. A rule that generates a statement about the parent element (Listing 2); C. Rules that generate statements about what the successor elements are (Listing 3); D. Rules that generate resource property statements corresponding to element attributes (Listing 4). Listing 5 shows RML rules for the Sphere element that generate statements for the properties "a" (indicating that the resource is an instance of a class), x3do:hasParent, x3do:radius, and rules for the element Shape intended for statements about the property x3do:hasGeometry.

Representation

10.7910/dvn/gfrwu5

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APPLICATION OF NON-RELATIONAL DATABASES IN THE SOCIAL NETWORKS

BRAIN BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE (2019)

Miroslav Dechev Miroslav Galabov Tsvetanka Georgieva‐Trifonova

The purpose of this research is to present knowledge in the field of work of databases that contain semi-structured data, also known as NoSQL, in applications using big data.The characteristics of the main categories of the non-relational databases and some of their typical representatives (MongoDB, Redis) have been reviewed from the point of view of using them for storage and analysis of data in the social networks.The article presents a study, conducted on the already existing research in regards to the implementation of the non-relational databases in the social networks.Some of the perspectives of the future research are outlined as well.

Social network (sociolinguistics)

10.18662/brain/05

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Dataset for "Semantic web technologies for big data modeling from analytics perspective: a systematic literature review"

Harvard Dataverse (2021)

Tsvetanka Georgieva‐Trifonova Miroslav Galabov

This dataset contains the titles, abstracts, keywords, conclusions of the publications included in the systematic literature review (SLR) about Semantic web technologies for big data modeling from analytics perspective.

10.7910/dvn/ur6own

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Citations (1)

Review_nodes.txt

Harvard Dataverse (2019)

Tsvetanka Georgieva‐Trifonova Miroslav Galabov Donika Valcheva Teodor Kalushkov

The nodes of type Review represent the customer reviews. Their attributes are: number – serial number; reviewText – text of a customer review; category – the category with which the customer review is associated; onlineStore – the online store to which the customer review refers; source – the source from which the customer review is found; reviewMonth – the month and year of the customer review posting; webAddress – the web address of the customer review.

10.7910/dvn/kwibmm/7gnwgy

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Fractal image compression

Miroslav Galabov

The paper gives and introduction on Image Coding based on Fractals and develops a simple algorithm to be used as a reference design.

Fractal compression

Fractal transform

10.1145/973620.973673

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Citations (22)

Estimation DSP processor's performance

Miroslav Galabov

10.1145/365143.365320

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Research Problems and Methods in Big Data Analytics for Knowledge Discovery from Customer Feedback

2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (2021)

Tsvetanka Georgieva‐Trifonova Miroslav Galabov Martin Djonov

In the present paper, the recent research on the usage of big data analytics methods in knowledge discovery from the customer feedback is examined. The aim is to distinguish and summarize the main cases of application; to indicate the necessary data types and the corresponding appropriate methods. In addition, big data analytics methodologies are studied and a framework is proposed for adaption of the big data analytics process to support extracting useful information from customer feedback.

Data Analysis

Software analytics

10.1109/ismsit52890.2021.9604613

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Dataset for Graph-Based Presentation of Customer Reviews for Online Stores Term_nodes.txt

Harvard Dataverse (2019)

Tsvetanka Georgieva‐Trifonova Miroslav Galabov Donika Valcheva Teodor Kalushkov

The nodes of type Term represent the terms derived from the customer reviews after applying the pre-processing. Their attributes are: termName – a term; termOcc – the count of the occurrences of a term termName in all documents of the collection; nTerm – the count of the documents where termName occurs at least once; it is initialized to 0 and updated after the initial data loading.

Presentation (obstetrics)

10.7910/dvn/kwibmm/2t5ma1

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Queries_retrieving_term_weights_utf8-1.txt

Harvard Dataverse (2019)

Tsvetanka Georgieva‐Trifonova Miroslav Galabov Donika Valcheva Teodor Kalushkov

Retrieving the necessary data for training text mining models.

10.7910/dvn/kwibmm/zo5j8w

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TERM_CATEGORY_relationships.txt

Harvard Dataverse (2019)

Tsvetanka Georgieva‐Trifonova Miroslav Galabov Donika Valcheva Teodor Kalushkov

The relationships of type TERM_CATEGORY serve to store the values used to modify the weights in the vector space model. Their attribute pmi is the value of PMI (pointwise mutual information) measure of a term termName in regard to category categoryName.

10.7910/dvn/kwibmm/dah8oa

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