Abstract The growing elderly population in smart home environments necessitates increased remote medical support and frequent doctor visits. To address this need, wearable sensor technology plays a crucial role in designing effective healthcare systems for the elderly, facilitating human–machine interaction. However, wearable technology has not been implemented accurately in monitoring various vital healthcare parameters of elders because of inaccurate monitoring. In addition, healthcare providers encounter issues regarding the acceptability of healthcare parameter monitoring and secure data communication within the context of elderly care in smart home environments. Therefore, this research is dedicated to investigating the accuracy of wearable sensors in monitoring healthcare parameters and ensuring secure data transmission. An architectural framework is introduced, outlining the critical components of a comprehensive system, including Sensing, Data storage, and Data communication (SDD) for the monitoring process. These vital components highlight the system's functionality and introduce elements for monitoring and tracking various healthcare parameters through wearable sensors. The collected data is subsequently communicated to healthcare providers to enhance the well-being of elderly individuals. The SDD taxonomy guides the implementation of wearable sensor technology through environmental and body sensors. The proposed system demonstrates the accuracy enhancement of healthcare parameter monitoring and tracking through smart sensors. This study evaluates state-of-the-art articles on monitoring and tracking healthcare parameters through wearable sensors. In conclusion, this study underscores the importance of delineating the SSD taxonomy by classifying the system's major components, contributing to the analysis and resolution of existing challenges. It emphasizes the efficiency of remote monitoring techniques in enhancing healthcare services for the elderly in smart home environments.

Entity resolution (ER) is the operation of distinguishing records that return to the same real world entity. It is used to link records among datasets and to match query records in real-time with existing datasets. Indexing is a major step in the ER process that reduces the search space. Most existing indexing techniques that are utilized in the ER process are designed to work with English datasets. Such techniques may not be suitable for use with other languages, such as Arabic. In this paper, enhancement for indexing techniques that are designed to work with English datasets has been proposed to be used with Arabic language by applying transliteration on Arabic strings before performing the indexing step of the ER process. The proposed approach is experimented and compared with using word stems as blocking keys in the indexing step. The results show better matching accuracy for the use of transliteration over the use of words stems.

Recent research on pattern discovery has progressed from mining frequent patterns and sequences to mining structured patterns, such as trees and graphs. Graphs as general data structure can model complex relations among data with wide applications in web exploration and social networks. However, the process of mining large graph patterns is a challenge due to the existence of large number of subgraphs. In this paper, we aim to mine only frequent complete graph patterns. A graph g in a database is complete if every pair of distinct vertices is connected by a unique edge. Grid Complete Graph (GCG) is a mining algorithm developed to explore interesting pruning techniques to extract maximal complete graphs from large spatial dataset existing in Sloan Digital Sky Survey (SDSS) data. Using a divide and conquer strategy, GCG shows high efficiency especially in the presence of large number of patterns. In this paper, we describe GCG that can mine not only simple co-location spatial patterns but also complex ones. To the best of our knowledge, this is the first algorithm used to exploit the extraction of maximal complete graphs in the process of mining complex co-location patterns in large spatial dataset.

Graphs are widely used for modeling complicated data such as social networks, chemical compounds, protein interactions, XML documents and multimedia databases. To be able to effectively understand and utilize any collection of graphs, a graph database that efficiently supports elementary querying mechanisms is crucially required. Supergraph query is an important type of graph queries which has many practical applications. Given a graph database D, the answer set of a supergraph query q is computed by retrieving all graphs in D which are fully contained in q. A primary challenge in computing the answers of graph queries is that pair-wise comparisons of graphs are usually hard problems. For example, subgraph isomorphism is known to be NP-complete. Clearly, the success of any graph database application is directly dependent on the efficiency of the graph indexing and query processing mechanisms. In this paper, we study the problem of using the relational infrastructure to achieve an efficient evaluation of supergraph queries. We rely on an effective and efficient layer of features-based summary structures, called graph features knowledge, to reduce the required number of pair-wise graph comparisons and boost the efficiency of query processing. Finally, we conduct an extensive set of experiments on real and synthetic data sets to demonstrate the efficiency and the scalability of our approach.

This paper presents a systematic approach to mine co- location patterns in Sloan Digital Sky Survey (SDSS) data. SDSS Data Release 5 (DR5) contains 3.6 TB of data. Availability of such large amount of useful data is an opportunity for application of data mining techniques to generate interesting information. The major reason for the lack of such data mining applications in SDSS is the unavailability of data in a suitable format. This work illustrates a procedure to obtain additional galaxy types from an available attributes and transform the data into maximal cliques of galaxies which in turn can be used as transactions for data mining applications. An efficient algorithm GridClique is proposed to generate maximal cliques from large spatial databases. It should be noted that the full general problem of extracting a maximal clique from a graph is known as NP-Hard. The experimental results show that the GridClique algorithm successfully generates all maximal cliques in the SDSS data and enables the generation of useful co-location patterns.

Time Series are ubiquitous, hence, similarity search is one of the biggest challenges in the area of mining time series data. This is due to the vast data size, number of sequences and number of dimensions that lead to a very costly querying process. In this paper, we demonstrate, for the first time, the use of three dimensionality reduction techniques (random projection (RP), Down sampling (DS) and Averaging (Avg)) in time series similarity searches. Two different similarity measurements are used for this investigation; dynamic time warping (DTW) and Euclidean distance. A thorough study has been conducted in this paper based on very exhaustive experiments. Results show the individual performance of Avg, RP, and DS in the two similarity measurements in different dimensions. Simulation shows that a high similarity matching accuracy can still be achieved after a significant dimension reduction onto lower dimensions.