The past decade has seen a lot of research on various time series representations. Various researches have been carried out that focused on representations that are processed in batch mode and visualize each value with almost equal dependability. As the tremendous usage of mobile devices and real time sensors has released the necessity and importance for representations that can simultaneously be updated, and can estimate the time oriented data with reliability and proportional to its time period for extended analysis. The approximation property of time series data allows us to answer queries more effectively about the recent data with higher precision, since in many domains recent information is more useful than older information. We call such incoming data as amnesic. However we have to fetch the required information from amnesic data as it consists of greater value for data analysis. In this paper, we introduce a novel approach of time series analysis that can summarize the incoming streaming data and represent the processed streams as user-specified amnesic functions. We propose algorithms for monitoring and handling streaming time series data and summarizing them for performing user driven analysis. As our focus is on handling streaming data and summarizing the streams, we suggest that processed streams to be forwarded to appropriate visualization and plot them in streaming visualization.
I. INTRODUCTION
Recent advances in both hardware and software have allowed huge rise in streaming data processing. However, handling massive amounts of data and arriving in continuous streams poses a challenge for researchers and practitioners, due to the physical limits of the various handy and computational resources. We have seen a gro...
... middle of paper ...
...n, Kaushik Chakrabarti, Michael Pazzani, and Sharad Mehrotra. "Dimensionality reduction for fast similarity search in large time series databases." Knowledge and information Systems 3, no. 3 (2001): 263-286.
[28] Palpanas, Themis, Michail Vlachos, Eamonn Keogh, and Dimitrios Gunopulos. "Streaming time series summarization using user-defined amnesic functions."Knowledge and Data Engineering, IEEE Transactions on 20, no. 7 (2008): 992-1006.
[29] Silva, Jonathan A., Elaine R. Faria, Rodrigo C. Barros, Eduardo R. Hruschka, André CPLF de Carvalho, and João Gama. "Data stream clustering: A survey."ACM Computing Surveys (CSUR) 46, no. 1 (2013): 13.
[30] Aigner, Wolfgang, Silvia Miksch, Wolfgang Muller, Heidrun Schumann, and Christian Tominski. "Visual methods for analyzing time-oriented data."Visualization and Computer Graphics, IEEE Transactions on 14, no. 1 (2008): 47-60.
Big Data is characterized by four key components, volume, velocity, variety, and value. Furthermore, Big Data can come from an array sources such as Facebook, Twitter, call
...means and become familiar with K-means clustering and its usage. Then, we finish this part by different method of clustering. The K-nearest- neighbors is also discussed in this chapter. The KNN is simple for implication, programming, and one of the oldest techniques of data clustering as well. There are many applications existing for KNN and it is still growing. The PCA also discussed in this chapter as a method for dimension reduction, and then discrete wavelet transform is discussed. For the next chapter the combination of PCA and DWT, which can be useful in de-noising, come about. In this study, we have examined the neural network structure and modeling that is most of usage these days. The backpropagation is one of the common methods of training neural networks and for the last model, we discussed autoregressive model and the strategies to choose a model order.
I organized my times, trails, and averages into a table and a graph to present my information.
According to Lisa Arthur, big data is as powerful as a tsunami, but it’s a deluge that can be controlled. In a positive way it provides business insights and value. Big data is data that exceeds the processing capacity of conventional database systems. It is a collection of data from traditional and digital sources inside and outside a company that represents a source of ongoing discovery and analysis. The data is too big, moves to fast, or doesn’t fit the structures of the database architecture. Daily, we create 2.5 quintillion bytes of data. In the last couple years we have created 90% of data we have in the world. This data comes from many places like climate information, social media sites, pictures or videos, purchase transaction records, cell phone GPS signals, and many more places. From the beginning of recorded time through 2003 users created 5 billion gigabytes of data. 2011, the same amount was created every couple days. 2013, we created that same amount every ten minutes. Some users prefer to constrain big data into digital inputs like web behavior and social network interactions. The data doesn’t exclude traditional data that is from product transaction information, financial records and interaction channels.
“Traditionally, scientists have looked for the simplest view of the world around us. Now, mathematics and computer powers have produced a theory that helps
Data aggregation is a type of data mining and/or processing of data that is not meaningful in its form, where it can be transformed into meaningful information that can be related to a specific person or thing. Another definition of data aggregation is “data aggregation is a type of data and information mining process where data is searched, gathered and presented in a report-based, summarized format to achieve specific business objectives or processes and/or conduct human analysis.” Data aggregation is a double sword edge that can be devoted for good or bad proposes. After attack of September 11,2001 a private company that owned by Hank Asher, has developed a complex algorithm that search and s...
Principal Component Analysis (PCA) is a multivariate analysis performed in purpose of reducing the dimensionality of a multivariate data set in order to recognize the shape or pattern of that data set. In other words, PCA is a powerful technique for pattern recognition that attempts to explain the variance of a large set of inter-correlated variables. It indicates the association between variables, thus, reducing the dimensionality of the data set. (Helena et al, 2000; Wunderlin et al, 2001; Singh et al, 2004)
...g system that supports the scalability of their data. The following is their input on their new proposal to create a new operational insight tool in order to provide a solution to their challenge:
We live in a world that can’t live without binary code anymore. Computers have pervaded so deep in our lives that they are now being called ubiquitous. With phenomenal increase in users, has come a phenomenal increase in data. We generate a vast amount of data through activities on our computing devices making it necessary to employ intelligent algorithms which enable the system to learn from and analyze this vast dataset. Fortunately, the advent of Distributed Computing has created avenues to access virtually limitless computing power even through mobile devices thus, allowing us to use highly complex and large scale algorithms. However, with all this power under the hood, it is important to make the computers as usable and receptive to users as possible. I believe this interdisciplinary paradigm will have far reaching impact on industries, governments as well as our daily lives which is why I am so interested in research concerning Information Management and Analytics, Artificial Intelligence, Human Computer Interaction, and Mobile and Internet Computing.
Big Data is a term used to refer to extremely large and complex data sets that have grown beyond the ability to manage and analyse them with traditional data processing tools. However, Big Data contains a lot of valuable information which if extracted successfully, it will help a lot for business, scientific research, to predict the upcoming epidemic and even determining traffic conditions in real time. Therefore, these data must be collected, organized, storage, search, sharing in a different way than usual. In this article, invite you and learn about Big Data, methods people use to exploit it and how it helps our life.
Abstract—Computational problems have significance from the early civilizations. These problems and solutions are used for the study of universe. Numbers and symbols have been used for different fields e.g. mathematics, statistics. After the emergence of computers the number and objects needs to be arranged in a particular order i.e. ascending and descending orders. The ordering of these numbers is generally referred to as sorting. Sorting gained a lot of importance in computer sciences and its applications are in file systems etc. A number of sorting algorithms have been proposed with different time and space complexities. In this paper author will propose a new sorting algorithm i.e. Relative Split and Concatenate Sort, implement the algorithm and then compared results with some of the existing sorting algorithms. Algorithm’s time and space complexity will also be the part of this paper.
This transition is depicted through the progression of time in the document.
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
...ch Reips. ““Big Data”: Big Gaps of Knowledge in the Field of Internet Science.” International Journal of Internet Science 7.1 (2012): n. pag. Web. 16 Mar. 2014.
Big data will then be defined as large collections of complex data which can either be structured or unstructured. Big data is difficult to notate and process due to its size and raw nature. The nature of this data makes it important for analyses of information or business functions and it creates value. According to Manyika, Chui et al. (2011: 1), “Big data is not defined by its capacity in terms of terabytes but it’s assumed that as technology progresses, the size of datasets that are considered as big data will increase”.