The amount of data is growing sharply on the Internet. Some data like log files are enormous and entail valuable and precious hidden patterns. In other words, a log file is a set of recorded events that carry beneficial and vital information to develop web server performance, stability server loads, control, and rush up user response operations. However, analyzing massive data take a long time and require powerful hardware. Also, the performance of sequential pattern mining methods is usually unsatisfactory to deal with such data. This paper proposes a novel and advanced parallel method for finding the log file patterns, such as frequent patterns (e. g., URL, IP, Status Code ), how users accessed files, the number of errors, and the most common errors by applying the Apache Spark platform. Experiment results demonstrate that the proposed method's run time on three datasets is significantly less than its four rival pattern mining methods.

RDF MODELS ARE WIDELY USED IN THE WEB OF DATA DUE TO THEIR FLEXIBILITY AND SIMILARITY TO GRAPH PATTERNS. BECAUSE OF GROWING USE OF RDFS, THEIR VOLUMES AND CONTENTS ARE INCREASING. THEREFORE, PROCESSING OF SUCH AMOUNT OF DATA ON A SINGLE MACHINE IS NOT EFFICIENT ENOUGH, BECAUSE OF THE RESPONSE TIME AND LIMITED HARDWARE RESOURCES. AS A RESULT, TO PROCESS THIS DATA MODEL, CLUSTER PROCESSING IS INTRODUCED. ONE OF THESE CLUSTER PROCESSING TOOLS IS APACHE HADOOP. BECAUSE OF USING TOO MUCH OF HARD DISKS, THE RESPONSE TIME IS USUALLY UNACCEPTABLE. IN THIS PAPER, ACCORDING TO THIS PROBLEM, WE USE APACHE SPARK FOR RAPID PROCESSING OF RDF DATA MODELS. ONE KEY FEATURE OF APACHE SPARK IS USING MAIN MEMORY INSTEAD OF HARD DISK, SO THE SPEED OF DATA PROCESSING IS IMPROVED. IN CONTINUES, WE WILL RUN SQL QUERY ON RDF DATA WHICH PARTITIONED ON THE CLUSTER.

Using a mix of machine learning algorithms and big data tools, particularly Apache Spark and also Apache Kafka, this research provides a new method for real-time blood pressure prediction. The method can handle large amounts of inbound data from numerous sources, including wearable technology and internet of things monitors. A clustering-based approach is used to improve the blood pressure estimation's precision while the data is being analyzed in real-time. ECG, PPG, and ABP signals dataset are used to assess the suggested strategy, and the findings show a substantial improvement in blood pressure prediction accuracy when compared to previous methods. The suggested method has the potential to be used in numerous uses, such as remote patient tracking, individualized healthcare, and cardiovascular disease early detection. This research offers two contributions. First off, it introduces a novel technique for real-time blood pressure forecast that is more accurate than current approaches. In addition, it shows the value of merging machine learning techniques with real-time streaming data processing systems like Apache Spark and Apache Kafka. Further improving the scalability and accuracy of the system is the use of web-based tools and deep learning methods. The suggested method may have a big impact on how well patients do and how much it will cost to treat them. Overall, this research offers a path that can be useful to both individuals and healthcare professionals for the creation of real-time blood pressure forecast tools.

TODAY, THERE ARE MANY DATA SOURCES DUE TO THE INCREASED USE OF SMART DEVICES, WHICH ARE RESPONSIBLE FOR CONNECTING, COLLECTING, EXCHANGING AND TRANSMITTING THIS DATA VOLUME. IN ADDITION, RESEARCH SHOWS THAT BY THE YEAR 2030 ABOUT A TRILLION SENSORS WILL CONNECT TO THE INTERNET OF THINGS, WHICH WILL COLLECT AND TRANSMIT A LARGE AMOUNT OF DATA. THEREFORE, THERE IS A NEED TO USE LARGE DATA APPLICATIONS IN THE IOT. THESE TECHNOLOGIES ARE INTERDEPENDENT AND MUST BE DEVELOPED TOGETHER. IN THIS PAPER, WE REVIEW SOME OF THE CHALLENGES AND ISSUES OF LARGE DATA, AS WELL AS THE RESEARCH DONE BY OTHER RESEARCHERS. IN THE FOLLOWING, WE EXAMINE THE TWO MAJOR FRAMEWORKS IN THE LARGE DATA AND COMPARE THEM, AND ULTIMATELY EXAMINE THE REQUIREMENTS, AS WELL AS REVIEW THE ANALYTICAL SOLUTIONS IN THIS AREA.

The amount of data generated today regarding volume, generation velocity, and variety is quite immense. This, in turn, has created a great challenge for scientists and researchers. To devise a solution, researchers have suggested a variety of schemes to help alleviate this problem. One of the suggested schemas is Association Rule Mining, and it is primarily focused on finding the associations in transaction-like data. To assist in finding such associations, Frequent Itemsets should be discovered first. Therefore, this research is a new approach to finding Frequent Itemsets and it is based on the Apriori algorithm and Apache Spark distributed platform. Further, we introduce an extended version of Apriori which tends to find Maximal Frequent Itemsets first to help speed up the mining process. The results and comparison to algorithms like YAFIM and HFIM and the original Apriori show the suggested algorithm outperforms them in dense datasets by an average of 38 percent.