Abstrato

Visual Exploration of Amnesic Time Series Data Streams

Kaushal Chauhan, Mukta Takalikar

Time Series data is a time oriented data, where each data item refers to a specific point measured typically at successive instances in time space. Streaming data is real time, potentially massive, rapid sequence of data information arriving continuously in ordered sequence of items. Various researches have been carried out that focused on representations which are processed in batch mode and visualize each value with almost equal dependability. In many domains recent information is more useful than older information. We call such incoming data as amnesic as it consists of greater value for data analysis. The dissertation proposed a novel system to monitor streaming amnesic time series data, handle data streams by using sliding window and memory management methods, summarizing the amnesic data with the help of weighted moving average algorithm. Final phase includes visualizing amnesic and summarized data streams in the form of dynamic line chart visualization and generating the reports of summarized data as snapshots, which eventually facilitates analysts to recognize various patterns underlying streaming time series data.