Deep clustering (DC), a fusion of deep representation learning and clustering, has recently demonstrated positive results in data science, particularly text processing and computer vision. However, joint optimization of feature learning and data distribution in the multi-dimensional space is domain-specific, so existing DC methods struggle to generalize to other application domains (such as data integration). In data management tasks, where high-density embeddings and overlapping clusters dominate, a data management-specific DC algorithm should be able to interact better with the data properties to support data integration tasks. This paper presents a deep clustering algorithm for tabular data (TableDC) that reflects the properties of data management applications that cluster tables (schema inference), rows (entity resolution) and columns (domain discovery). To address overlapping clusters, TableDC integrates Mahalanobis distance, which considers variance and correlation within the data, offering a similarity method suitable for tabular data in high-dimensional latent spaces. TableDC also shows higher tolerance to outliers through its heavy-tailed Cauchy distribution as the similarity kernel. The proposed similarity measure is particularly beneficial where the embeddings of raw data are densely packed and exhibit high degrees of overlap. Data integration tasks may also involve large numbers of clusters, which challenges the scalability of existing DC methods. TableDC learns data embeddings with a large number of clusters more efficiently than baseline DC methods, which scale in quadratic time. We evaluated TableDC with several existing DC, Standard Clustering (SC), and state-of-the-art bespoke methods over benchmark datasets. TableDC consistently outperforms existing DC, SC and bespoke methods.

World Wide Web (WWW) is playing a vital role for sharing dynamic knowledge in every field of life. The information on web comprises a huge amount of data in different forms such as structured, semi structured, or few is totally in unstructured format. Due to huge size of information, searching from larger textual data about the specific topic or getting precise information is a challenging task. All this leads to the problem of word sense ambiguity (WSA). Urdu language-based information retrieval system using different techniques related to Web Semantic Search Engine architecture is proposed to efficiently retrieve the relevant information and solve the problem of WSA. The proposed system has average precision ratio 96% as compared to average precision ratio of 74% and 75% average precision Google for single word query. For the long text queries, our system outperforms the existing famous search engines with 92% accuracy such as Bing and Google having 16.50% and 16% accuracy, respectively. Similarly, the proposed system for single word query, the recall ratio is 32.25% as compared to 25% and 25% of Bing and Google. The results of recall ratio for long text query are improved as well, showing 6.38% as compared to 6.20% and 4.8% of Bing and Google, respectively. The results showed that the proposed system gives better and efficient results as compared to the existing systems for Urdu language.

Abstract The evolution of 4G telecommunication propagated various resource‐crunched clients to experience rate‐effective resources at ease. However, it extends its underlying centralised architecture, which arouses various challenges correlated with network data availability, network information protection, and operational infrastructure charges. With the recent revolution of telecommunication, 5G networks promised to provide credible schemes like the high quality of service, ultra‐low latency, and much security over the pre‐existing architecture. However, the deployment of end‐to‐end 5G network cutting‐edge systems in the present heterogeneous world limits its core idea of extensive data privacy, native interoperability, risk‐free interference, and radio spectrum sharing. Perhaps, to achieve its true capability, improved versions of blockchain technology could be aligned to strengthen various real‐time complex applications at a flourishing rate. One of the multiplexed real‐time enterprise applications is a keyword search engine where the integrity of user data files and keyword searches are bound to come under cyber hackers. On the one hand, it was found that when a 5G‐based blockchain emulated network gets deployed with intact encryption techniques, the entire system facilitates to give reliable, efficient, and risk‐free keyword search over variegated 5G network data and its complex computational calculations. Consequently, the use of blockchain‐based decentralised cloud orchestration scheme at various levels enabled the architecture to remain incorruptible and protects all the confidential files and keywords in a fully controlled file access environment. The results of the simulation kernel shows that proposed architecture which, when combined with blockchain‐based decentralised cloud orchestration network system, justify all the essential characteristics and effectuates the optimal use of 5G network sharing by each network entity.