A Web Smart Space Framework for Intelligent Search Engines

A web smart space is an intelligent environment which has additional capability of searching the information smartly and efficiently. New advancements like dynamic web contents generation has increased the size of web repositories. Among so many modern software analysis requirements, one is to search information from the given repository. But useful information extraction is a troublesome hitch due to the multi-lingual; base of the web data collection. The issue of semantic based information searching has become a standoff due to the inconsistencies and variations in the characteristics of the data. In the accomplished research, a web smart space framework has been proposed which introduces front end processing for a search engine to make the information retrieval process more intelligent and accurate. In orthodox searching anatomies, searching is performed only by using pattern matching technique and consequently a large number of irrelevant results are generated. The projected framework has insightful ability to improve this drawback and returns efficient outcomes. Designed framework gets text input from the user in the form complete question, understands the input and generates the meanings. Search engine searches on the basis of the information provided

Insights of Medication Adherence Management: A Qualitative Study with Healthcare Professionals and Technology Designers

Poor Medication Adherence (MA) remains a major challenge to ensuring good patient health. This qualitative study examines health professionals’ and health technology designers’ insights for improving this problem using mHealth. We interviewed twenty-three New Zealand health professionals and analysed the interviews using content analysis. In this paper, we discuss the four main themes that emerged from our analysis: 1) patient characteristics, 2) collaboration among members of the health team, 3) medication impact including effectiveness and side effects 4) technology acceptance. We present a conceptual model to visually summarise MA issues from healthcare professionals\u27 and technology designers’ perspective

Co-Designing a Medication Notification Application with Multi-Channel Reminders

Evidence suggests that medication adherence applications (apps) are one of the most effective methods to remind patients to take medication on time. Reminders via apps are overwhelming today, consumers discard using them after a brief period of initial usage, eventually becoming unfavourable towards them and not using them at all. This study aims to qualitatively determine the key features and design of medication reminder apps that facilitate or disrupt usage from the users perceptive. Three focus groups were conducted with participants aged between 15 and 65+ (N= 12). The participants evaluated a smart medication reminder prototype, then sketched and discussed their thoughts and perceptions within the group. Participants identified, 1) Multi-channel reminders, 2) Medication intake acknowledgement for reporting and 3) Seamless addition of medications and associated reminders as important elements. Understanding consumers needs and concerns will inform the future development of medication reminder apps that are acceptable and valuable to consumers

Optimising HYBRIDJOIN to Process Semi-Stream Data in Near-real-time Data Warehousing

Near-real-time data warehousing plays an essential role for decision making in organizations where latest data is to be fed from various data sources on near-real-time basis. The stream of sales data coming from data sources needs to be transformed to the data warehouse format using disk-based master data. This transformation process is a challenging task due to slow disk access rate as compare to the fast stream data. For this purpose, an adaptive semi-stream join algorithm called HYBRIDJOIN (Hybrid Join) is presented in the literature. The algorithm uses a single buffer to load partitions from the master data. Therefore, the algorithm has to wait until the next disk partition overwrites the existing partition in the buffer. As the cost of loading the disk partition into the buffer is a major cost in the total algorithm’s processing cost, this leaves the performance of the algorithm sub-optimal. This paper presents optimisation of existing HYBRIDJOIN by introducing another buffer. This enables the algorithm to load the second buffer while the first one is under join execution. This reduces the time that the algorithm wait for loading of master data partition and consequently, this improves the performance of the algorithm significantly

TOPICAL EXPRESSIVITY IN SHORT TEXTS

With each passing minute, online data is growing exponentially. A bulk of such data is generated from short text social media platforms such as Twitter. Such platforms are fundamental in social media knowledge-based applications like recommender systems. Twitter, for example, provides rich real-time streaming information. Extracting knowledge from such short texts without automated support is not feasible due to Twitter\u27s platform streaming nature. Therefore, an automated method for comprehending patterns in such text is a need for many knowledge systems. This paper provides solutions to generate topics from Twitter data. We present several techniques related to topical modelling to identify topics of interest in short texts. Topic modelling is inherently problematic in shorter texts with very sparse vocabulary in addition to the informal language used in their dissemination. Such findings are informative in knowledge extraction for social media-based recommender systems as well as in understanding tweeters over time

Parallelisation of a cache-based stream-relation join for a near-real-time data warehouse

Near real-time data warehousing is an important area of research, as business organisations want to analyse their businesses sales with minimal latency. Therefore, sales data generated by data sources need to reflect immediately in the data warehouse. This requires near-real-time transformation of the stream of sales data with a disk-based relation called master data in the staging area. For this purpose, a stream-relation join is required. The main problem in stream-relation joins is the different nature of inputs; stream data is fast and bursty, whereas the disk-based relation is slow due to high disk I/O cost. To resolve this problem, a famous algorithm CACHEJOIN (cache join) was published in the literature. The algorithm has two phases, the disk-probing phase and the stream-probing phase. These two phases execute sequentially; that means stream tuples wait unnecessarily due to the sequential execution of both phases. This limits the algorithm to exploiting CPU resources optimally. In this paper, we address this issue by presenting a robust algorithm called PCSRJ (parallelised cache-based stream relation join). The new algorithm enables the execution of both disk-probing and stream-probing phases of CACHEJOIN in parallel. The algorithm distributes the disk-based relation on two separate nodes and enables parallel execution of CACHEJOIN on each node. The algorithm also implements a strategy of splitting the stream data on each node depending on the relevant part of the relation. We developed a cost model for PCSRJ and validated it empirically. We compared the service rates of both algorithms using a synthetic dataset. Our experiments showed that PCSRJ significantly outperforms CACHEJOIN

Wind farms selection using geospatial technologies and energy generation capacity in Gwadar

Pakistan has been a victim of energy crisis since last few decades. This energy crisis has adversely affected country’s socio-economic development and continues to do so. The continuously increasing demand–supply gap has negatively impacted the economic stability of the country. With the recent awareness and development of alternate energy resources like wind and solar, the current energy crisis can be minimized. However, proper planning is essential for successful execution of these renewable energy projects. This study aims to identify the suitable sites for wind farms in District Gwadar, Balochistan using Geographical Information Systems (GIS) and Web-based Spatial Decision Support System (SDSS). In this study, multi-criteria decision making is applied which assists breaking down the site selection complexity. Multi-Criteria evaluation methods provides different set of procedures that facilitate decision making by analyzing different alternatives. The underlying geospatial and ICT technologies used in this analysis form the core component of the planning process. Gwadar is currently drawing investor’s attention due to its geographical location, deep seaport, and proposed China–Pakistan Economic Corridor (CPEC). This research is useful for stakeholders of Wind Energy to explore the wind potentials using GIS as an interactive decision-making tool during the pre-feasibility stage.Furthermore, this research has considered the environmental, social and economic aspects during the decision-making process of wind farm development. This is the strength of multi-criteria evaluation as differently weighted scenarios provide different output, depending on the factors considered of highest importance. A detailed analysis of the sites in terms of their wind potential and energy generation capacity has also been reported in this study. This long coastline of Balochistan with huge wind energy potential has not been explored yet and therefore this study will assist researchers to further explore this area and can have a positive impact on CPEC.Qatar University Internal Grant No. IRCC- 2021-010

Big data velocity management-from stream to warehouse via high performance memory optimized index join

Efficient resource optimization is critical to manage the velocity and volume of real-time streaming data in near-real-time data warehousing and business intelligence. This article presents a memory optimisation algorithm for rapidly joining streaming data with persistent master data in order to reduce data latency. Typically during the transformation phase of ETL (Extraction, Transformation, and Loading) a stream of transactional data needs to be joined with master data stored on disk. To implement this process, a semi-stream join operator is commonly used. Most semi-stream join operators cache frequent parts of the master data to improve their performance, this process requires careful distribution of allocated memory among the components of the join operator. This article presents a cache inequality approach to optimise cache size and memory. To test this approach, we present a novel Memory Optimal Index-based Join (MOIJ) algorithm. MOIJ supports many-to-many types of joins and adapts to dynamic streaming data. We also present a cost model for MOIJ and compare the performance with existing algorithms empirically as well as analytically. We envisage the enhanced ability of processing near-real-time streaming data using minimal memory will reduce latency in processing big data and will contribute to the development of highperformance real-time business intelligence systems