Mining and Linking Patterns across Live Data Streams and Stream Archives
نویسندگان
چکیده
We will demonstrate the visual analytics system V istream , that supports interactive mining of complex patterns within and across live data streams and stream pattern archives. Our system is equipped with both computational pattern mining and visualization techniques, which allow it to not only efficiently discover and manage patterns but also effectively convey the mining results to human analysts through visual displays. In our demonstration, we will illustrate that with V istream , analysts can easily submit, monitor and interact with a broad range of query types for pattern mining. This includes novel strategies for extracting complex patterns from streams in real time, summarizing neighbourbased patterns using multi-resolution compression strategies, selectively pushing patterns into the stream archive, validating the popularity or rarity of stream patterns by stream archive matching, and pattern evolution tracking to link patterns across time.
منابع مشابه
Mining Frequent Patterns in Uncertain and Relational Data Streams using the Landmark Windows
Todays, in many modern applications, we search for frequent and repeating patterns in the analyzed data sets. In this search, we look for patterns that frequently appear in data set and mark them as frequent patterns to enable users to make decisions based on these discoveries. Most algorithms presented in the context of data stream mining and frequent pattern detection, work either on uncertai...
متن کاملIncremental Mining of Across-streams Sequential Patterns in Multiple Data Streams
Sequential pattern mining is the mining of data sequences for frequent sequential patterns with time sequence, which has a wide application. Data streams are streams of data that arrive at high speed. Due to the limitation of memory capacity and the need of real-time mining, the results of mining need to be updated in real time. Multiple data streams are the simultaneous arrival of a plurality ...
متن کاملMining Sequential Patterns Across Data Streams
There are extensive endeavors toward mining frequent items or itemsets in a single data stream, but rare efforts have been made to explore sequential patterns among literals in different data streams. In this paper, we define a challenging problem of mining frequent sequential patterns across multiple data streams. We propose an efficient algorithm MILE to manage the mining process. The propose...
متن کاملCASW: Context Aware Sliding window for Frequent Itemset Mining over Data Streams
In recent years, advances in both hardware and software technologies coupled with high-speed data generation has led to data streams and data stream mining. Data generation has been much faster in data stream applications and scores of data is generated in quick turnaround time. Hence it becomes obvious to perform mining, data on arrival that is usually termed as data stream mining. General fre...
متن کاملAnalytical Data Mining for Stream Data Analysis
The main idea behind this research relies on analytical data mining functions to handle data streams. Given the characteristics of the data stream, the new methods and techniques for stream data analysis must conduct advanced analysis and data mining over fast and large data streams to capture the trends, patterns and exceptions. Besides, much of such data resides at rather low level of abstrac...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- PVLDB
دوره 6 شماره
صفحات -
تاریخ انتشار 2013