Samir SELLAMI (@SamRepository)
github

SamRepository

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  • Stars
    star
    9
  • Global Rank 1,034,169 (Top 36 %)
  • Followers 3
  • Following 3
  • Registered almost 7 years ago
  • Most used languages
    C#
         66.7 %
    CSS
         33.3 %
  • Location πŸ‡©πŸ‡Ώ Algeria
  • Country Total Rank 681
  • Country Ranking
    C#
         31
    CSS
         219

Top repositories

1

odoo_dev

My custom Odoo modules repository
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MidSemI

MidSemI is a mediator-based integration system that integrate information and searches for entities about persons, organizations, and products in internal enterprise CRM relational database and link it with semi-structured Facebook social network data and structured DBPedia linked data.
C#
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3

KGMap

KGMap: Knowledge Graph Mapping Model is a Middelware for leveraging knowledge graphs by bridge between heterogeneous relational, social and linked data. It consists of three sub-models: the knowledge graph representation model, the sources metadata de-scription model, and the mapping model. KGMap maintains these models and defines different mapping cases. The main goal is to assist an expert identifying mapping candidates relying on an algorithm which calculate a similarity measure to determine relatedness between the sources metadata elements and knowledge graph schema elements. The implementation of the KGMap is a service-oriented middleware that relates entities about persons, organizations, and products from private enterprise CRM relational database with semi-structured Facebook social data and DBpedia dataset.
C#
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4

Covid-19_Algeria-Dashboard

Algeria Coronavirus COVID-19 Dashboard
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5

Web_Technologies_MindMap

A comprehensive mind map covering various aspects of web technologies, from basic internet concepts to advanced development practices.
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6

KeyFS

KeyFS: Keyword-based Faceted Search over integrated data. The engine in KeyFS is based on our MidSemI middleware and our KGMap semantic mapping approach. KeyFS is equipped with a reactive faceted browsing interface designed to facilitate the exploration of integrated data behind the knowledge graph.
CSS
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7

K-Means_Clustering

This is an unsupervised cluster analysis for identifying groups of approaches most similar in functionality to MidSemI. We evaluated the integration systems in related works section against the assessment presented in Table 3. The results are summarized in Table 4. In order to achieve this purpose, we transformed Table 4 into a matrix with numerical values that denote normalized distances (in the range [0-1]) among features, values close to 1 indicate proximity to the best implementation of this feature or dimension. We calculated distances related to dimensions by summing distances of all the features belongs to given dimension divided by their number. Figure 14 (a) reports the distances values for both features and dimensions. We employed *Weka* [https://www.cs.waikato.ac.nz/ml/weka/] which is a well-known machine learning analysis tool to perform the clustering. We applied the K-Means clustering algorithm with K=4. By clustering ten systems to four clusters, we aim at identifying a medium groups of systems which share similar function and to reveal dependencies encoded in the vector representation. Figure 14 (b) depicts the clustering results. The first cluster includes MidSemI along with FuhSen and LDIF systems. The second cluster contains OD CleanStore and LSM tools. The Third cluster holds MOMIS and SWIS systems. The rest of systems Mastro, KRAFT and OBSERVER form the last cluster. We generated the positions of clusters randomly, and the radius of each bubble is proportional to the sum of features distances for each system. Figure 15 (a) and (b) plots comparison of features and dimensions coverage respectively concerning only the first cluster that holds systems closest to our MidSemI approach (FuhSen and LDIF).
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