Mining frequent sequential patterns in data streams using SSM-algorithm.

Frequent sequential mining is the process of discovering frequent sequential patterns in data sequences as found in applications like web log access sequences. In data stream applications, data arrive at high speed rates in a continuous flow. Data stream mining is an online process different from traditional mining. Traditional mining algorithms work on an entire static dataset in order to obtain results while data stream mining algorithms work with continuously arriving data streams. With rapid change in technology, there are many applications that take data as continuous streams. Examples include stock tickers, network traffic measurements, click stream data, data feeds from sensor networks, and telecom call records. Mining frequent sequential patterns on data stream applications contend with many challenges such as limited memory for unlimited data, inability of algorithms to scan infinitely flowing original dataset more than once and to deliver current and accurate result on demand. This thesis proposes SSM-Algorithm (sequential stream mining-algorithm) that delivers frequent sequential patterns in data streams. The concept of this work came from FP-Stream algorithm that delivers time sensitive frequent patterns. Proposed SSM-Algorithm outperforms FP-Stream algorithm by the use of a hash based and two efficient tree based data structures. All incoming streams are handled dynamically to improve memory usage. SSM-Algorithm maintains frequent sequences incrementally and delivers most current result on demand. The introduced algorithm can be deployed to analyze e-commerce data where the primary source of the data is click stream data. (Abstract shortened by UMI.)Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .M668. Source: Masters Abstracts International, Volume: 44-03, page: 1409. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Mining very long sequences with PLWAPLong algorithms

Sequential pattern mining is the process of finding inter-transaction frequent sequential patterns from a sequential database, where records consist of ordered sets of events (or items), by applying data mining techniques on such sequential databases. Discovering sequential patterns in web server logs is an example application of sequential mining, which is useful for predicting visiting patterns of web users for such purposes as targeted advertisements. Position Coded Pre-order Linked Web Access Pattern (PLWAP) mining algorithm is one of the existing efficient web sequential pattern mining algorithms, which stores the frequently stored sequences of the entire sequential database in a compressed tree form with position coded nodes. However, for very long sequences exceeding thirty two nodes, the number of bits an integer position code can hold, the PLWAP algorithm\u27s performance begins to degrade because it employs linked lists to store conjunctions of long position codes and the linked list traversals slow down the algorithm both during tree construction and mining. PLWAP algorithm also uses each and every node in the frequent 1-item event queue to test for that event inclusion in the suffix tree root set during mining. This is a very expensive operation since except for one node all other nodes that are its ancestors and descendents are not included in the root set. This thesis proposes two new algorithms, i.e. PLWAPLong1 and PLWAPLong2. Both of these new algorithms use a new position code numbering scheme where each node is assigned two numeric variables (startPosition, endPosition) instead of one. Using this scheme we can determine the ancestor node in O (1) operation by comparing the startPosition and endPosition of two nodes. PLWAPLong1 algorithm also proposes transforming the linked list based tree to an equivalent array representation and using binary search to find the immediate descendant in a suffix tree. PLWAPLong2 uses existing linked list based tree. Both PLWAPLong1 and PLWAPLong2 algorithms introduce a new technique called Last Descendant to eliminate the unwanted nodes from ancestor/descendent test when creating the suffix tree root set. Keywords: Data mining, Web Mining, Association Rule Mining, Long Sequences, PLWAP Minin

IDEAS-1997-2021-Final-Programs

This document records the final program for each of the 26 meetings of the International Database and Engineering Application Symposium from 1997 through 2021. These meetings were organized in various locations on three continents. Most of the papers published during these years are in the digital libraries of IEEE(1997-2007) or ACM(2008-2021)

Simulated role-playing from crowdsourced data

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 173-178).Collective Artificial Intelligence (CAl) simulates human intelligence from data contributed by many humans, mined for inter-related patterns. This thesis applies CAI to social role-playing, introducing an end-to-end process for compositing recorded performances from thousands of humans, and simulating open-ended interaction from this data. The CAI process combines crowdsourcing, pattern discovery, and case-based planning. Content creation is crowdsourced by recording role-players online. Browser-based tools allow nonexperts to annotate data, organizing content into a hierarchical narrative structure. Patterns discovered from data power a novel system combining plan recognition with case-based planning. The combination of this process and structure produces a new medium, which exploits a massive corpus to realize characters who interact and converse with humans. This medium enables new experiences in videogames, and new classes of training simulations, therapeutic applications, and social robots. While advances in graphics support incredible freedom to interact physically in simulations, current approaches to development restrict simulated social interaction to hand-crafted branches that do not scale to the thousands of possible patterns of actions and utterances observed in actual human interaction. There is a tension between freedom and system comprehension due to two bottlenecks, making open-ended social interaction a challenge. First is the authorial effort entailed to cover all possible inputs. Second, like other cognitive processes, imagination is a bounded resource. Any individual author only has so much imagination. The convergence of advances in connectivity, storage, and processing power is bringing people together in ways never before possible, amplifying the imagination of individuals by harnessing the creativity and productivity of the crowd, revolutionizing how we create media, and what media we can create. By embracing data-driven approaches, and capitalizing on the creativity of the crowd, authoring bottlenecks can be overcome, taking a step toward realizing a medium that robustly supports player choice. Doing so requires rethinking both technology and division of labor in media production. As a proof of concept, a CAI system has been evaluated by recording over 10,000 performances in The Restaurant Game, automating an Al-controlled waitress who interacts in the world, and converses with a human via text or speech. Quantitative results demonstrate how CAI supports significantly more open-ended interaction with humans, while focus groups reveal factors for improving engagement.by Jeffrey David Orkin.Ph.D

Mining Web Sequential Patterns Incrementally with Revised PLWAP Tree

Abstract. Since point and click at web pages generate continuous data stream, which flow into web log data, old patterns may be stale and need to be updated. Algorithms for mining web sequential patterns from scratch include WAP, PLWAP and apriori-based GSP. An incremental technique for updating already mined patterns when database changes, which is based on an efficient sequential mining technique like the PLWAP is needed. This paper proposes an algorithm, Re-PL4UP, which uses the PLWAP tree structure to incrementally update web sequential patterns. Re-PL4UP scans only the new changes to the database, revises the old PLWAP tree to accommodate previous small items that have become large and previous large items that have become small in the updated database without the need to scan the old database. The approach leads to improved performance