Tools in data science for better processing

Analysing the data is an important part of a research in data science. There are many tools that can be used in analysing a data set to get the experiment results for classification, clustering and others. However, the researchers are concerned about how to increase the efficiency in analysing a data set. In this paper, three open source tools which are the Waikato Environment for Knowledge Analysis (WEKA), Konstanz Information Miner (KNIME) and Salford Predictive Modular (SPM) were compared to identify the better processing tools in evaluating the presented data. All of these tools have their own different characteristics. WEKA can handle pre-processing of data and then analyses it based on different algorithms. It is suitable to be used for classification, regression, clustering, association rules, and visualisation. The algorithms can be applied directly to a data set or called from its own Java code. KNIME is more inclined towards producing graphical view, while SPM is a highly accurate and ultra-fast analytics which also data mines platforms for any sizes, complexity or organisation. The results illustrate the tools capability in analysing data sets and evaluators in an efficient and effective manner

Usability testing on intelligent mobile web pre-fetching of cloud storage scheme

Mobile device and Cloud Storage (CS) represent the trends of technology usage of the last few years. However, the difficulty in managing the data when there are too many simultaneous uses of cloud storage services at the same time that can cause latency or delayed time. This paper evaluates mobile cloud storage services using usability testing, which is intended to access by multiple of Cloud Storage Services (CSS) with the proposed Intelligent Mobile Web Pre-fetching of Cloud Storage Scheme (MOBICS). The results show most of the respondents with 95.65% agreeing that MOBICS system was very practical and has enhanced the speed in accessing and storing data by Mobile Cloud Storage (MCS). Besides, MOBICS reduces time of interaction up to 19.28% for the local pre-fetching and 18.80% for the intelligent pre-fetching

Management system prototype for intelligent mobile cloud computing for big data

The current challenge of mobile devices is the storage capacity that has led service providers to develop new value-added mobile services. To address these limitations, mobile cloud computing, which offers on-demand is developed. Mobile Cloud Computing (MCC) is developed to augment device capabilities, facilitating to mobile users store, access to a big dataset on the cloud. Even so, given the limitations of bandwidth, latencies, and device battery life, new responses are required to extend the use of mobile devices. This paper presents a novel design and implementation of developing process on intelligent mobile cloud storage management system, also called as Intelligent Mobile Cloud Computing (IMCC) for android based users. IMCC is important for cloud storage user to make their data effectively and efficiently for saving the user time. IMCC provided convenience for user to use multiple cloud storage using one application and easy for users to store their data to any cloud storage. The result shows using IMCC it only took 8 seconds to access the data, which is faster compared with traditional MCC, it took 23.33 seconds. IMCC reduce 65.71% of latency occur using the MCC in managing a user data. The developed IMCC prototype is accessible through the Google Play Store

Usability Study on Mobile Web Pre-Fetching

Web pre-fetching is a technique that can increase the speed of web loading process. It refers to the operation of fetching information from remote web servers even before it is requested. This research approves a web pre-fetching method that is capable to decrease user-perceived latency in web browsing for mobile environment, in which it is essential as mobile devices have several limitations compared to normal desktop PC. Advanced Keystroke Level Model (KLM) technique was used where it predicted the interaction time of user with Mobile Web Pre-Fetching (MWeP) prototype and it is validated through the statistical method of single factor controlled experiments where user testing was done. Facebook was chosen as the case study where the usability evaluation based on advanced KLM technique supported that user access time with MWeP prototype decreased considerably while the usability evaluation through user testing implied that the mobile web pre-fetching prototype was satisfactory to user in terms of learnability, efficiency of use, memorability, error frequency and satisfaction. This research indicated that the utilisation of web pre-fetching is recommended in mobile environment as it effectively decreases user interaction time during web browsing that enhances user’s overall browsing experience. Based on advanced KLM technique, the total access time for mobile application with MWeP method decreased than those without MWeP

Mobile intelligent web pre-fetching scheme for cloud computing services in industrial revolution 4.0

Currently the Mobile Cloud Computing (MCC) rapidly development and being important as the amount of consumers retrieving information continues to grow over time. Every day when customers request the information, large data storage must serve large volume data operations. Therefore, to fix the inadequate information storage encountered by some suppliers, intelligent techniques are need. In addition, the worldwide industry has altered in many areas in recent years owing to excellent technological advances. The Industry 4.0 concept has developed in this modern period and afterward has been embraced and examined by both; scholarly and professionals in numerous other progressed nations. Hence, an enhancement Mobile Intelligent Web Pre-fetching Scheme was proposed to offer a management of cloud data storage for users to readily access the information with quickness wherever to prevent the amount of response time during user access the information. This paper examines the challenges of Cloud Computing (CC) innovation in industry 4.0. The intelligent web pre-fetching technique is utilize to upgrade the execution of Cloud Computing (CC) when taking care of information get to by clients. An improvement of the MCC scheme is propose to bolster information administration to provide proficient and viable execution of MCC services for industry 4.0

Usability testing on intelligent mobile web pre-fetching of cloud storage scheme

Mobile device and Cloud Storage (CS) represent the trends of technology usage of the last few years. However, the difficulty in managing the data when there are too many simultaneous uses of cloud storage services at the same time that can cause latency or delayed time. This paper evaluates mobile cloud storage services using usability testing, which is intended to access by multiple of Cloud Storage Services (CSS) with the proposed Intelligent Mobile Web Pre-fetching of Cloud Storage Scheme (MOBICS). The results show most of the respondents with 95.65% agree that MOBICS system is useful and has enhanced the speed in accessing and storing data by Mobile Cloud Storage (MCS). Besides, MOBICS reduces time of interaction up to 19.28% for the local pre-fetching and 18.80% for the intelligent pre-fetching

Scalability of mobile cloud storage

Today, there are high demands on Mobile Cloud Storage (MCS) services that need to manage the increasing number of works with stable performance. This situation brings a challenge for data management systems because when the number of works increased MCS needs to manage the data wisely to avoid latency occur. If latency occurs it will slow down the data performance and it should avoid that problem when using MCS. Moreover, MCS should provide users access to data faster and correctly. Hence, the research focuses on the scalability of mobile cloud data storage management, which is study the scalable on how deep the data folder itself that increase the number of works

Mobile cloud computing architecture on data management for big data storage

The rapid development of Mobile Cloud Computing (MCC) in this era is very significant, as the number of users accessing data keeps growing with time. Large data storage has to serve high volume transactions of data everyday when users request the data, therefore intelligent methods are required to solve the insufficient data storage experienced by some providers. Hence, a pre-fetching technique and Machine Learning (ML) technique are used to provide cloud data storage management for users to easily access their data anywhere at high speed to avoid latency; the total of response time during user access the data. This paper discusses the use of MCC technology with the pre-fetching technique to overcome the issue of latency in data management when there are large amounts of data being stored in the cloud. The pre-fetching technique is used to optimise the performance of Cloud Computing (CC) when handling data access by users. An enhancement of the MCC architecture is proposed to support data management for the efficient and effective performance of MCC services

Scalability of Mobile Cloud Storage

Today, there are high demands on Mobile Cloud Storage (MCS) services that need to manage the increasing number of works with stable performance. This situation brings a challenge for data management systems because when the number of works increased MCS needs to manage the data wisely to avoid latency occur. If latency occurs it will slow down the data performance and it should avoid that problem when using MCS. Moreover, MCS should provide users access to data faster and correctly. Hence, the research focuses on the scalability of mobile cloud data storage management, which is study the scalable on how deep the data folder itself that increase the number of works

Similarity Identification of Large-scale Biomedical Documents using Cosine Similarity and Parallel Computing

Document similarity computation is an important research topic in information retrieval, and it is a crucial issue for automatic document categorization. The similarity value is between 0 and 1, then the closest value to 1 is represented both documents is considered more relevant, vice versa. However, the large scale of textual information has created the problem of finding the relevance level between documents. Therefore, the relevance between mesh heading text in the PubMed documents is higher than the relevance of the abstract text in the PubMed documents. Furthermore, parallel computing is implemented to speed up the large-scale documents similarity identification process that automatically calculates in the PubMed application. The execution time of mesh heading is 15.447 seconds, and the timely execution of abstract is 74.191 seconds. The execution time of mesh heading is higher than abstract because abstract contains more words than mesh heading. This study has successfully identified the similarity between large-scale biomedical documents of the PubMed documents that implemented a cosine similarity algorithm. The result has shown that the cosine similarity of the mesh heading texts is higher than the abstract text in the form of a graph and table shown in the PubMed application. The cosine similarity is useful to measure the similarity between documents based on the TF*IDF calculation result