SigmaWay Blog

A Geographic Information System (GIS) integrates hardware, software, and data for capturing, managing, analyzing, and displaying all forms of geographically referenced information. GIS allows us to view, understand, question, interpret, and visualize data in many ways that reveal relationships, patterns, and trends in the form of maps, globes, reports, and charts. It helps you answer questions and solve problems by looking at your data in a way that is quickly understood and easily shared. There are a lot of advantages of using GIS technology. It makes data handling easier, covers large area, used for monitoring various things because of repetitive coverage, it is fast, can be used in inaccessible areas, unbiased, more accurate, reliable and economical. Data could be collected in several bands/ colors so it could be used for micro level analysis. GIS will help in decision making by government officials and in increasing transparency and accountability for good governance. It will help in management of natural resources, improved allocation of resources and planning, improved communication during crisis, cost savings by improved decision makings etc.

The effective use and implementation of Radio-frequency identification (RFID), GPS & data mining techniques in Public Distribution System (PDS) can facilitate PDS supply chain and promise eradicating mismanagement, corruption, trafficking, theft and anti-social elements. RFID provides highly accurate and detailed information by capturing the data and information at each stage of the supply chain, automatically. It also improves the safety and efficiency of the food supply chain. Location technology GPS can also be combined with RFID technology to automatically track and record the information regarding the field where the produce was picked, when and where it was transported and the current location of the produce. This also helps in reducing theft and trafficking. Data mining techniques based on the rule base classification model is used to identify the suspicious moving behavior of the objects. To read more: http://www.articlesbase.com/information-technology-articles/control-of-public-distribution-system-using-gps-gis-remote-sensing-with-data-mining-rfid-3393327.html

Text mining, which is sometimes referred to "text analytics", is one way to make qualitative or "unstructured" data usable by a computer. Also known as text data mining, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, sentiment analysis etc. Text analysis involves information retrieval, analysis to study word frequency distributions, pattern recognition, tagging, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The main goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods. To read more about text mining: http://www.scientificcomputing.com/blogs/2014/01/text-mining-next-data-frontier.

Fraudsters are more competent, better made, and creatively excellent than whatever possible time in the later past. Their adulteration arrangements include complex frameworks of individuals, records, and events. The evidence for these schemes may exist on multiple systems, incorporate various data sorts, and deliberately represent hidden activity. So an analyst has abundant investigative focuses on these frameworks with no true approach to join data or results. To prevent and uncover deception, one needs a solution that is more exceptional and advanced than hoaxers. A basic venture in fraud detection analytics is visualizing the patterns in your data between people, places, frameworks, and events. These data mining and profound analysis capabilities provide more context and better information, enabling more accurate data segmentation and data labelling, which further improves pattern recognition. To read more about it: http://www.21ct.com/solutions/fraud-detection-analytics/.

Professional sports organizations are multi-million dollar enterprises with millions of dollars spent on a single decision. With this amount of capital at stake, just one bad or misguided decision has the potential of setting an organization back by several years. With such a large amount of risk involved it requires a critical need to make good decisions, and hence it’s an attractive environment for data mining applications.

Sports Data Mining has experienced rapid growth in recent years. The task is not how to collect the data, but what data should be collected and how to make the best use of it. From players improving their game-time performance using video analysis techniques, to scouts using statistical analysis and projection techniques to identify what talent will provide the biggest impact, data mining is quickly becoming an integral part of the sports decision making landscape where managers and coaches using machine learning and simulation techniques can find optimal strategies for an entire upcoming season. By finding the right ways to make sense of data and turning it into actionable knowledge, sports organizations have the potential to secure a competitive advantage over their peers. To read more how it has been used: http://www.ukessays.com/essays/psychology/data-mining-in-sports-in-the-past-few-years-psychology-essay.php 

This article elaborates on the sentiment analysis from tweets using data mining techniques. Instead of using SQL, it shows how to conduct such analysis using a more sophisticated software called RapidMiner. It explains how one can extract Twitter data into Google Docs spread sheet and then transfer it into a local environment utilizing two different methods. The emphasis is on how to amass a decent pool of tweets in two different ways using a service called Zapier, Google Docs and a tool called GDocBackUpCMD, along with SSIS and a little bit of C#. Zapier is used to extract Twitter feeds into Google Docs spread sheet and then copy the data across to local environment to mine it for sentiment trends. Next, it is shown how this data can be analyzed for sentiments i.e. whether a concrete Twitter feed can be considered as negative or positive. For this purpose, RapidMiner as well as two separate data sets of already pre-relegated tweets for model learning and Microsoft SQL Server for some data polishing and storage engine. Read more at:http://bicortex.com/twitter-sentiment-analysis-mining-twitter-data-using-rapidminer-part-1/

Companies do a lot of expenditure on resources to analyze big data. But surprisingly, in most cases they fail to address the question of why they are doing so. Many times they use different high end big data software unnecessarily when there is a simpler solution. Before starting the analysis of big data, a proper data thinking approach is necessary. This will not only save company’s time and money, but also optimize resources. So before mining big data, we have to think analytically with some small data. It is data thinking that can prove to be really big.

Robert D. Behn, lecturer at Harvard University’s John F. Kennedy School of Government, talks more about defining a proper question before analyzing Big Data here:http://www.govexec.com/excellence/promising-practices/2014/04/got-big-data-first-define-your-big-question/81661/?oref=river .   

The last five years, we have seen a massive upsurge of data; we have generated 90 percent of the entire world's data in the last two years!!! Why today analytics is so successful? Just because of availability of data? No! We have to admit the technological advancement we made through the last decade to analyze the available data, also the success can be attributed to the decreased cost of storage space. Twenty years ago, we had all the data analysis tools in place but data was scarce. Today there is a frightening abundance of data... But then what to do with this available data and how! Yeah we got the techniques, but we need the applicability mapping... Think about the applicability of analytics into the telecom sector, which represents an agile supply chain management ...These examples points to the tremendous possibilities that big data offer in telecom sector. But we must never forget the underlying secret: only blue-blooded telecom experts can usher in this exciting telecom future. Today's big buzzword is big data. Its companion, buzzword analytics, is also a big favorite. There are good reasons why this is so. 

For More Information Go To This Link:

http://www.business-standard.com/article/management/a-secret-about-analytics-114030200628_1.html

The numbers are coming in thick and fast but can big data and advanced analytics influence a team’s performance in the Soccer Premier Leagues?

Since the 1950s, when retired RAF pilot Charles Reep’s roughly-researched analytics led to the long-ball strategy, the football community has been enthralled by statistics. Today Premier League teams and their followers are being inundated with data as high-tech cameras capture every pass, dribble, free kick or touch of the ball while monitoring players’ xy coordinates every tenth of a second throughout the 90-minute match! What we call it as?

For more information check the following link:

http://knowledge.insead.edu/operations-management/beyond-moneyball-data-mining-the-premier-league-3155

In recent years, data mining has been used widely in the areas of science and engineering, such as bioinformatics, genetics, medicine, and electrical power engineering. Several researchers and organizations have conducted reviews of data mining tools and surveys of data miners. These identify some of the strengths and weaknesses of the software packages. They also provide an overview of the behaviors, preferences and views of data miners. How about reaching a few inches deep into the applicability and usability of data mining which is the analysis stage of a typical KDD in terms of Education?

Please Follow this link

http://www.thehindu.com/news/cities/chennai/harnessing-data-mining-to-revolutionise-education/article5690365.ece