Efficient Subgraph Mining Algorithm On Big Data Essay

A set of experiments have been conducted and proved that the graph database provides a promising result. The graph approach is applied in two different methods. The sub graph approach and path finding approach. In the sub graph approach the data structures that repeat often are compared where as in path finding approach finite length search is performed. Data in the databases are written using various methods with ILP (Inductive Logic Programming) being prominent. Concept discovery involves searching for the target data given a background of facts. Association rule mining is used in relational concept discovery. Association rule mining is finding frequent patterns, associations or correlations among sets of items or objects in databases. Relational association rules are expressed as query extensions in first-order logic. Hence in the method we present a hybrid graph-based discovery of data involving both graph substructure method and path finding

Abstract- This research documents a comprehensive evaluation of the emerging graph databases along with a benchmark study to compare it to the existing relational model. With the ease of the graphical representation brought in with Neo4j, we saw the opportunity to attempt getting details about the various attributes in the dataset and analyze this data to present a statistical view along with its popular counterpart, MySQL. The ultimate goal of this study is to determine whether a traditional relational database system like MySQL, can be replaced completely in production, by a graph database, such as Neo4j.

The author points out that although there are existing algorithms and tools available to handle Big Data, they are not sufficient as the volume of data is exponentially increasing every day. To show the usefulness of Big Data mining, the author highlighted the work done by United Nations. In order to further enhance the reader’s perspective, the author provided research work of various professionals to educate its readers about the most recent updates in Big Data mining field. The author further describes the controversies surrounding Big Data. The author has first provided the context and exigence by elaborating on why we need new algorithm and tools to explore the Big Data. The author used the strategy of highlighting the logos by mentioning the research work of different industry professionals, workshops conducted on Big Data and was able to appeal to connect to the reader’s ethos. The author also used pathos by urging the budding Big Data researchers to further dig deep into the topic and explore this area

Srinivasan and Arunasalam (2103) said that health care sector mostly deals with extremely large amounts of health data related to patients. This structured or unstructured data which is obtained from various sources with different velocities is known as big data. By combining, organizing and processing this data using an architectural framework, big data analytics is possible. This paper describes the potential of this big data analytics in health care. It discusses the outlines and benefits of using an architectural framework to analyze big data in health care and explains how it can improve the efficiency of health care. It also briefly discusses

Healthcare contain huge amount of data which becomes essential for performance and planning.For every organization data is important for gaining the knowledge,annotation,research.So for healthcare big data is one of the solution for potential impact.A programming model Map Reduce processes the data and with help of this we can analyze unstructured data into structured data. The Map-Reduce based approach is used for data cube materialization and mining over massive datasets.

However, in our age we have resources that haven’t always been available to solve these problems. Electronic health care records, the internet, mobile phones and apps are a few of the many technologies that contribute to the copious amount of data we have access to today. This large amount of data can be overwhelming. While it once seemed impossible to sort through and extract meaningful patterns from these enormous amounts of data, automated data processing techniques have significantly advanced.

In recent years the amount of data accumulated from social networks has become very large, and there is a lot of valuable information to gain from analyzing and applying data mining to social network data.

Social network analysis is used to study the pattern of communication and relationships among the members of a social network. The interaction on the social network is assumed to be reflective of how the individual interacts and is an insight into the members’ behavior patterns (Costa, 2012). The graph theory has been used effectively to understand a variety of unrelated problems from organizational behavior to spread of infectious diseases. In social network research, various algorithms have been designed in conjunction with concepts from relational learning, web mining and inductive logic to perform predictive as well as descriptive analysis. As described in Karamon (2008) some of

Big data is the new and still relatively misunderstood phenomenon in which companies are you using vast amounts of collected data to reveal patterns and certain trends within their collected data. Though big data is being used in a variety of different fields from retail to governmental uses, it is becoming most prominent within the healthcare field. Everyday thousands of people are admitted into hospitals and seen at various emergency clinics around the world. What if all this data from each individual seen at these clinics and hospital could be accumulated and a detailed report given to see signs of new diseases or new trends of medications effects? This is where big data is becoming such an integral part within the healthcare field.

Abstract—Fast Distributed Mining (FDM) which generates a small number of candidate set and substantially reduce the number of messages to be passed at mining association rules. Distributed data mining offers a way by data can be shared without compromising privacy. The paper present secure protocols for the task of top-k subgroup discovery on horizontally partitioned data. In this setting, all sites use the same set of attributes and the quality of every subgroup depends on all databases. The approach finds patterns in the union of the databases, without disclosing the local databases. This is the first secure approach that tackles any of the supervised descriptive rule discovery tasks. It is simpler and significantly more efficient in terms of communication rounds, communication cost and computational cost.

An organisation(data owner) which lacks the expertise or computational resources required for data mining can outsource its data mining tasks to third party service provider(server).But there are various security issues associated with this kind of outsourcing because the server can misuse the data provide by the organisation directly or by extracting frequents patterns from it. However, both data and the association

The healthcare industry historically has generated large amounts of data, driven by record keeping, compliance & regulatory requirements, and patient care. Whilemost data is stored in hard copy form, the current trend istoward rapid digitization of these large amounts of data.Driven by mandatory requirements and the potential to improve the quality of healthcare delivery meanwhile reducingthe costs, these massive quantities of data (known as ‘big data’) hold the promise of supporting a wide rangeof medical and healthcare functions, including amongothers clinical decision support, disease surveillance and population health management Reports say data from the U.S. healthcare system alone reached, in 2011, 150 exabytes. At this rate of

The Facebook dataset [12] was present in the form of an edgelist. The dataset was read and its corresponding graph was generated, which serves as the original network representation. The random graphs – Erdos–Renyi (ER) random

Due to the rapid growth in the use of Internet and its connected tools, an enormous amount of data are being produced on a daily basis. The concept of big data arrives when we were unable to manage this huge data with traditional methods. Big data is a mechanism of capturing, storing and analyzing the big datasets and also an idea of extracting some value from it. It is very handful while determining the root causes of failures, issues and defects in near-real time, creating coupons and other sales offers according to the customers shopping patterns, detecting any suspicious and fraudulent activities in real-time. As it is very advantageous, it also has some issues. Some of the common issues can be characterized into heterogeneity, complexity, timeless, scalability and privacy. The most important and significant challenge in the big data is to preserve privacy information of the customers, employees, and the organizations. It is very sensitive and includes conceptual, technical as well as legal significance.

Big data has become enduring as cost effective approaches have emerged to five V’s in Big Data, the five V’s are: high Volume, high Velocity, high Variety, Veracity and Value of information. Within this data lie valuable patterns and information previously hidden because of the amount of work required extract them. In the era of technology the commodity hardware, cloud architechers and open source software bring Big data processing into the reach of the less well resourced. Big data processing is the eminently feasible even the small garage startups, who can cheaply rent server time in the cloud. The frame work to process and analyze stored Big data is named Hadoop. HDFS in Hadoop is used to store the data and Map Reduce is the tool to process the data. Hadoop ecosystem (including Pig, Hive, Mahout, and Hadoop), stream mining, complex-event processing, and NoSQL databases are enabling the analysis of large-scale, heterogeneous datasets at unprecedented scales and speeds. These technologies are transforming security analytics by facilitating the storage, maintenance, and analysis of security information. For instance, the WINE platform [1] and Bot-Cloud [2] allow the use of Map Reduce to efficiently process data for security analysis. Earlier Security Information and Event Management (SIEM)[3] tools were not developed