The sequential behavior pattern analysis of broiler chickens exposed to heat stress.

Abstract. Broiler productivity is dependent on a range of variables; among them, the rearing environment is a significant factor for proper well-being and productivity. Behavior indicates the bird´s initial response to an adverse environment and is capable of providing an indicator of well-being in real-time. The present study aims to identify and characterize the sequential pattern of broilers´ behavior when exposed to thermoneutral conditions (TNZ) and thermal stress (HS) by constant heat. The research was carried out in a climatic chamber with 18 broilers under thermoneutral conditions and heat stress for three consecutive days (at three different ages). The behavior database was first analyzed using one-way ANOVA, Tukey test by age, and Boxplot graphs, and then the sequence of the behaviors was evaluated using the generalized sequential pattern (GSP) algorithm. We were able to predict behavioral patterns at the different temperatures assessed from the behavioral sequences. Birds in HS were prostrate, identified by the shorter behavioral sequence, such as the {Lying down, Eating} pattern, unlike TNZ ({Lying down, Walking, Drinking, Walking, Lying down}), which indicates a tendency to increase behaviors (feeding and locomotor activities) that guarantee the better welfare of the birds. The sequence of behaviors 'Lying down' followed by 'Lying laterally' occurred only in HS, which represents a stressful thermal environment for the bird. Using the pattern mining sequences approach, we were able to identify temporal relationships between thermal stress and broiler behavior, confirming the need for further studies on the use of temporal behavior sequences in environmental controller

Event Correlated Usage Mapping in an Embedded Linux System - A Data Mining Approach

A software system composed of applications running on embedded devices could be hard to monitor and debug due to the limited possibilities to extract information about the complex process interactions. Logging and monitoring the systems behavior help in getting an insight of the system status. The information gathered can be used for improving the system and helping developers to understand what caused a malfunctioning behavior. This thesis explores the possibility of implementing an Event Sniffer that runs on an embedded Linux device and monitors processes and overall system performance to enable mapping between system usage and load on certain parts of the system. It also examines the use of data mining to process the large amount of data logged by the Event Sniffer and with this find frequent sequential patterns that cause a bug to affect the system’s performance. The final prototype of the Event Sniffer logs process cpu usage, memory usage, process function calls, interprocess communication, system overall performance and other application specific data. To evaluate the data mining of the logged information a bug pattern was planted in the interprocess communication, that caused a false malfunctioning. The data mining analysis of the logged interprocess communication was able to find the planted bug-patterna that caused the false malfunctioning. A search for a memory leak with the help of data mining was also tested by mining function calls from a process. This test found sequential patterns that was unique when the memory increased

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

Modeling Student Affective State Patterns during Self-Regulated Learning in Physics Playground

This dissertation research focuses on investigating the incidence of student self-regulated learning behavior, and examines patterns in student affective states that accompany such self-regulated behavior. This dissertation leverages prediction models of student affective states in the Physics Playground educational game platform to identify common patterns in student affective states during use of self-regulated learning behavior. In Study 1, prediction models of student affective states are developed in the context of the educational game environment Physics Playground, using affective state observations and computer log data that had already been collected as part of a larger project. The performances of student affective state prediction models generated using a combination of the computer log and observational data are then compared against those of similar prediction models generated using video data collected at the same time. In Study 2, I apply these affective state prediction models to generate predictions of student affective states on a broader set of data collected from students participants playing Physics Playground. In parallel, I define aggregated behavioral features that represent the self-observation and strategic planning components of self-regulated learning. Affective state predictions are then mapped to playground level attempts that contain these self-regulated learning behavioral features, and sequential pattern mining is applied to the affective state predictions to identify the most common patterns in student emotions. Findings from Study 1 demonstrate that both video data and interaction log data can be used to predict student affective states with significant accuracy. Since the video data is a direct measure of student emotions, it shows better performance across most affective states. However, the interaction log data can be collected natively by Physics Playground and is able to be generalized more easily to other learning environments. Findings from Study 2 suggest that self-regulatory behavior is closely associated with sustained periods of engaged concentration and .self-regulated learning behaviors are associated with transitions from negative affective states (confusion, frustration, and boredom) to the positive engaged concentration state. The results of this dissertation project demonstrate the power of measuring student affective states in real time and examining the temporal relationship to self-regulated learning behavior within an unstructured educational game platform. These results thus provide a building block for future research on the real-time assessment of student emotions and its relationship with self-regulated learning behaviors, particularly within online student-centered and self-directed learning contexts

データマイニングにおけるユーザベリティ向上と応用に関する研究

制度:新 ; 文部省報告番号:甲2574号 ; 学位の種類:博士(工学) ; 授与年月日:2008/3/15 ; 早大学位記番号:新473

Methods for frequent sequence mining with subsequence constraints

In this thesis, we study scalable and general purpose methods for mining frequent sequences that satisfy a given subsequence constraint. Frequent sequence mining is a fundamental task in data mining and has many real-life applications like information extraction, market-basket analysis, web usage mining, or session analysis. Depending on the underlying application, we are generally interested in discovering certain frequent sequences, which are described using subsequence constraints. There exists many tools and algorithms for this task, however, they are not sufficiently scalable to deal with large amounts of data that may arise in applications and are generally not extensible across range of applications. We propose scalable, distributed sequence mining algorithms that target MapReduce. Our work builds on MG-FSM, which is a distributed framework for frequent sequence mining. We propose novel algorithms that improve and extend the basic MG-FSM framework to efficiently support traditional subsequence constraints that arise in applications. Additionally, we show that many subsequence constraints---including and beyond the traditional ones considered in literature---can be unified in a single framework. A unified treatment allows researchers to study jointly many types of subsequence constraints (instead of each one individually) and helps to improve usability of pattern mining systems for practitioners. To this end, we propose a general purpose framework that provides a set of simple and intuitive ``pattern expressions'', which allows to describe any subsequence constraint of interest and explore algorithms for efficiently mining frequent subsequences under such general constraints. Our experimental study on real-world datasets indicates that our proposed algorithms are scalable and effective across wide range of applications

E-commerce Recommendation by an Ensemble of Purchase Matrices with Sequential Patterns

In E-commerce recommendation systems, integrating collaborative filtering (CF) and sequential pattern mining (SPM) of purchase history data will improve the accuracy of recommendations and mitigate limitations of using only explicit user ratings for recommendations. Existing E-commerce recommendation systems which have combined CF with some form of sequences from purchase history are those referred to as LiuRec09, ChioRec12, and HPCRec18. ChoiRec12 system, HOPE first derives implicit ratings from purchase frequency of users in transaction data which it uses to create user item rating matrix input to CF. Then, it computes the CFPP, the CF-based predicted preference of each target user_u on an item_i as its output from the CF process. Similarly, it derives sequential patterns from the historical purchase database from which it obtains the second output matrix of SPAPP, sequential pattern analysis predicted preference of each user for each item. The final predicted preference of each user for each item FPP is obtained by integrating these two matrices by giving 90\% to SPAPP and 10\% to CFPP so it can recommend items with highest ratings to users. A limitation of HOPE system is that in user item matrix of CF, it does not distinguish between purchase frequency and ratings used for CF. Also in SPM, it recommends items, regardless of whether user has purchased that item before or not. This thesis proposes an E-commerce recommendation system, SEERs (Stacking Ensemble E-commerce Recommendation system), which improves on HOPE system to make better recommendations in the following two ways: i) Learning the best minimum support for SPA, best k similar users for CF and the best weights for integrating the four used matrices. ii) Separating their two intermediate matrices of CFPP and SPAPP into four intermediate matrices of CF not purchased, SPM purchased, SPM not purchased and purchase history matrix which are obtained and merged with the better-learned parameters from (i) above. Experimental results show that by using best weights discovered in training phase, and also separating purchased and not purchased items in CF and sequential pattern mining methods, SEERS provides better precision, recall, F1 score, and accuracy compared to tested systems