International Journal of Advanced Computer Science and Applications, 2017
NGS (Next Generation Sequencing) technology has resulted in huge amount of proteomics data that e... more NGS (Next Generation Sequencing) technology has resulted in huge amount of proteomics data that exists in the form of interactions (protein-protein, gene-protein, and genedisease). ETL (Extraction, Transformation, and Loading) techniques are very useful for Databases. Existing Rational Databases are not unified and having SQL (Structured Query Language). Proteomics data requires improvement for Integration of different Data sources. With the usage of NoSQL (not only SQL), improve the efficiency and performance. For this, a novel based unified model has been designed for protein interactions data (P-P, G-G, and G-D) by using Apache HBase to evaluate given the model, different case studies have been used.
International Journal of Advanced Computer Science and Applications, 2017
Large Scale Graph Matching (LSGM) is one of the fundamental problems in Graph theory and it has a... more Large Scale Graph Matching (LSGM) is one of the fundamental problems in Graph theory and it has applications in many areas such as Computer Vision, Machine Learning, Pattern Recognition and Big Data Analytics (Data Science). Matching belongs to the combinatorial class of problems which refers to finding correspondence between the nodes of a graph or among set of graphs (subgraphs) either precisely or approximately. Precise Matching is also known as Exact Matching such as (sub)Graph Isomorphism and Approximate Matching is called Inexact Matching in which matching activity concerns with conceptual/semantic matching rather than focusing on structural details of graphs. In this article, a review of matching problem is presented i.e. Semantic Matching (conceptual), Syntactic Matching (structural) and Schematic Matching (Schema based). The aim is to present the current state of the art in Large Scale Graph Matching (LSGM), a systematic review of algorithms, tools and techniques along with the existing challenges of LSGM. Moreover, the potential application domains and related research activities are provided.
International Journal of Advanced Computer Science and Applications, 2017
Graph is an expressive way to represent dynamic and complex relationships in highly connected dat... more Graph is an expressive way to represent dynamic and complex relationships in highly connected data. In today's highly connected world, general purpose graph databases are providing opportunities to experience benefits of semantically significant networks without investing on the graph infrastructure. Examples of prominent graph databases are: Neo4j, Titan and OrientDB etc. In biological OMICS landscape, Interactomics is one of the new disciplines that focuses mainly on the data modeling, data storage and retrieval of biological interaction data. Biological experiments generate prodigious amount of data in various formats(semi-structured or unstructured). The large volume of such data posses challenges for data acquisition, data integration, multiple data modalities (either data model of storage model, storage, processing and visualization. This paper aims at designing a well suited graphical data storage model for biological information which is collected from major heterogeneous biological data repositories, by using graph database.
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Papers by hina farooq