On Generation of Firewall Log Status Reporter (SRr) Using Perl

Computer System Administration and Network Administration are few such areas where Practical Extraction Reporting Language (Perl) has robust utilization these days apart from Bioinformatics. The key role of a System/Network Administrator is to monitor log files. Log file are updated every day. To scan the summary of large log files and to quickly determine if there is anything wrong with the server or network we develop a Firewall Log Status Reporter (SRr). SRr helps to generate the reports based on the parameters of interest. SRr provides the facility to admin to generate the individual firewall report or all reports in one go. By scrutinizing the results of the reports admin can trace how many times a particular request has been made from which source to which destination and can track the errors easily. Perl scripts can be seen as the UNIX script replacement in future arena and SRr is one development with the same hope that we can believe in. SRr is a generalized and customizable utility completely written in Perl and may be used for text mining and data mining application in Bioinformatics research and development too.Comment: 10Page

MATURE-HEALTH: HEALTH Recommender System for MAndatory FeaTURE choices

Balancing electrolytes is utmost important and essential for appropriate functioning of organs in human body as electrolytes imbalance can be an indication of the development of underlying pathophysiology. Efficient monitoring of electrolytes imbalance not only can increase the chances of early detection of disease, but also prevents the further deterioration of the health by strictly following nutrient controlled diet for balancing the electrolytes post disease detection. In this research, a recommender system MATURE Health is proposed and implemented, which predicts the imbalance of mandatory electrolytes and other substances presented in blood and recommends the food items with the balanced nutrients to avoid occurrence of the electrolytes imbalance. The proposed model takes user most recent laboratory results and daily food intake into account to predict the electrolytes imbalance. MATURE Health relies on MATURE Food algorithm to recommend food items as latter recommends only those food items that satisfy all mandatory nutrient requirements while also considering user past food preferences. To validate the proposed method, particularly sodium, potassium, and BUN levels have been predicted with prediction algorithm, Random Forest, for dialysis patients using their laboratory reports history and daily food intake. And, the proposed model demonstrates 99.53 percent, 96.94 percent and 95.35 percent accuracy for Sodium, Potassium, and BUN respectively. MATURE Health is a novel health recommender system that implements machine learning models to predict the imbalance of mandatory electrolytes and other substances in the blood and recommends the food items which contain the required amount of the nutrients that prevent or at least reduce the risk of the electrolytes imbalance.Comment: Author version of the pape

Cloud and IoT-based emerging services systems

The emerging services and analytics advocate the service delivery in a polymorphic view that successfully serves a variety of audience. The amalgamation of numerous modern technologies such as cloud computing, Internet of Things (IoT) and Big Data is the potential support behind the emerging services Systems. Today, IoT, also dubbed as ubiquitous sensing is taking the center stage over the traditional paradigm. The evolution of IoT necessitates the expansion of cloud horizon to deal with emerging challenges. In this paper, we study the cloud-based emerging services, useful in IoT paradigm, that support the effective data analytics. Also, we conceive a new classification called CNNC {Clouda, NNClouda} for cloud data models; further, some important case studies are also discussed to further strengthen the classification. An emerging service, data analytics in autonomous vehicles, is then described in details. Challenges and recommendations related to privacy, security and ethical concerns have been discussed

Contextual motivation in physical activity by means of association rule mining

The primary thrust of this work is to demonstrate the applicability of association rule mining in public health domain, focusing on physical activity and exercising. In this paper, the concept of association rule mining is shown assisting to promote the physical exercise as regular human activity. Specifically, similar to the prototypical example of association rule mining, market basket analysis, our proposed novel approach considers two events – exercise (sporadic) and sleep (regular) as the two items of the frequent set; and associating the former, exercise event, with latter, the daily occurring activity sleep at night, helps strengthening the frequency of the exercise patterns. The regularity can further be enhanced, if the exercising instruments are kept in the vicinity of the bed and are within easy reach

Towards Querying and Visualization of Large Spatio-Temporal Databases

In any database model, data analysis can be eased by extracting a smaller set of the data of interest, called subset, from the mammoth original dataset. Thus, a subset helps enhance the performance of a system by avoiding the iteration through the huge parental data in further analysis. A subset, its specification, or the formal process for its extraction can be complex. In the database community, subsets are extracted through SQL-like queries and through visualization in the Geographic Information System (GIS) community. Both are iterative processes. An SQL query can be a composition of subqueries. Each subquery can be seen as an iterative step toward the extraction of the desired subset. For this to work, subqueries should result into relations that have the same structure as the relations in a given data model. Although it may not be immediately obvious, the visualization can be iterative too. Each community works in its own compartment. Either one uses subprocesses that are only subqueries or only visual interactions. Mixing these two subprocesses would yield a more powerful expressibility in the hands of users. Parametric Data Model is well-known for handling multidimensional parametric data, such as spatial, temporal, or spatio-temporal. In the parametric approach, the object is modeled as a single tuple, creating one-to-one correspondence between an object in the real world and a tuple in the database. The parametric approach relies on its own SQL-like, but richer, query language called ParaSQL which mimics the classical SQL. However, it is simpler and avoids self-join operations; hence, enhances performance. In the parametric approach, the attribute values are defined as a function, allowing large values, also. The execution of a query in the existing prototype of the Parametric Data Model results in data out, as stream in a raw text format that cannot be queried further. This is unlike classical databases, where a subset provides additional strength to a system and the prototype lacks this potential functionality. The real power of ParaSQL lies in the where clause, and previous versions of the prototype had a very simple implementation. It is expanded further in this research work to harness its hidden potential. To perform the preliminary investigation, exploratory visual analysis is an important aspect in any spatio-temporal database system. Previous versions of the prototype of Parametric Data Model completely lacked the visualization functionality. This work ensures the output of a ParaSQL (possibly a subset) will be a relation having the same format as relations in the model rather than plain text. It also attempts to expand the power of the where clause, ensuring a clean logic and more generic nature. Some important basic steps are taken to bring a visual in a way that is conducive to the structures in Parametric Data Model. The richness of GIS visualization serves as the foundation for the visual functionality of the Parametric Data Model. The query is executed on the parametric side, while the results are visualized on GIS side. This integration equips the Parametric Data Model with visualization functionality. GIS visualization also offers a click-based selection of a subset and its persistence, which later can be consumed by Parametric Data Model also. This research work establishes a two-way communication between the two communities-Parametric Data Model and GIS- where the output of one can serve as the input for the other and is an attempt to bring them together.</p