Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction Data

Learning to collaborate effectively requires practice, awareness of group dynamics, and reflection; often it benefits from coaching by an expert facilitator. However, in physical spaces it is not always easy to provide teams with evidence to support collaboration. Emerging technology provides a promising opportunity to make collocated collaboration visible by harnessing data about interactions and then mining and visualizing it. These collocated collaboration analytics can help researchers, designers, and users to understand the complexity of collaboration and to find ways they can support collaboration. This article introduces and motivates a set of principles for mining collocated collaboration data and draws attention to trade-offs that may need to be negotiated en route. We integrate Data Science principles and techniques with the advances in interactive surface devices and sensing technologies. We draw on a 7-year research program that has involved the analysis of six group situations in collocated settings with more than 500 users and a variety of surface technologies, tasks, grouping structures, and domains. The contribution of the article includes the key insights and themes that we have identified and summarized in a set of principles and dilemmas that can inform design of future collocated collaboration analytics innovations.     

A Wide Learning Approach for Interpretable Feature Recommendation for 1-D Sensor Data in IoT Analytics

Machine Intelligence Research - This paper presents a state of the art machine learning-based approach for automation of a varied class of Internet of things (IoT) analytics problems targeted on...     

PDViz: A Visual Analytics Approach for State Policy Data

Sub-national governments across the United States implement a variety of policies to address large societal problems and needs. Many policies are picked up or adopted in other states. This process is called policy diffusion and allows researchers to analyse and compare the social, political, and contextual characteristics that lead to adopting certain policies, as well as the efficacy of these policies once adopted. In this paper, we introduce PDViz, a visual analytics approach that allows social scientists to dynamically analyse the policy diffusion history and underlying patterns. It is designed for analysing and answering a list of research questions and tasks posed by social scientists in prior work. To evaluate our system, we present two usage scenarios and conduct interviews with domain experts in political science. The interviews highlight that PDViz provides the result of policy diffusion patterns that align with their domain knowledge as well as the potential to be a learning tool for students to understand the concept of policy diffusion.     

Intrusion Detection System for Big Data Analytics in IoT Environment

In the digital area, Internet of Things (IoT) and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic. Though IoT networks are popular and widely employed in real world applications, security in IoT networks remains a challenging problem. Conventional intrusion detection systems (IDS) cannot be employed in IoT networks owing to the limitations in resources and complexity. Therefore, this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning (IMFSDL) based classification model, called IMFSDL-IDS for IoT networks. The proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages: data transformation and data normalization. To manage big data, Hadoop ecosystem is employed. Besides, the IMFSDL-IDS model includes a hill climbing with moth flame optimization (HCMFO) for feature subset selection to reduce the complexity and increase the overall detection efficiency. Moreover, the beetle antenna search (BAS) with variational autoencoder (VAE), called BAS-VAE technique is applied for the detection of intrusions in the feature reduced data. The BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification performance. To validate the intrusion detection performance of the IMFSDL-IDS system, a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct aspects. The resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25% and 97.39% on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.     

A Conceptual Approach for Data Integration in Business Analytics

We present some basic concepts of a modelling environment for data integration in business analytics. Main emphasis is on defining a process model for the different activities occurring in connection with data integration, which allow later on assessment of the quality of the data. The model is based on combination of knowledge and techniques from statistical metadata management and from workflow processes. The modelling concepts are presented in a problem oriented formulation. The approach is embedded into an open model framework which aims for a modelling platform for all kinds of models useful in business applications.     

A Precomputed Approach for Real-Time Haptic Interaction with Fluids

The key to enhancing perception of the virtual world is improving mechanisms for interacting with that world. Through providing a sense of touch, haptic rendering is one such mechanism. Many methods efficiently display force between rigid objects, but to achieve a truly realistic virtual environment, haptic interaction with fluids is also essential. In the field of computational fluid dynamics, researchers have developed methods to numerically estimate the resistance due to fluids by solving complex partial differential equations, called the Navier-Stokes equations. However, their estimation techniques, although numerically accurate, are prohibitively time-consuming. This becomes a serious problem for haptic rendering, which requires a high frame rate. To address this issue, we developed a method for rapidly estimating and displaying forces acting on a rigid virtual object due to water. In this article, we provide an overview of our method together with its implementation and two applications: a lure-fishing simulator and a virtual canoe simulator     

面向大数据分析作业的启发式云资源供给方法

云计算已成为大数据分析作业的主流运行支撑环境,选择合适的云资源优化其性能面临巨大挑战.当前研究主要考虑大数据分析框架(如Hadoop,Spark等)的多样性,采用机器学习方法进行资源供给,但样本少容易陷入局部最优解.提出了大数据环境下基于负载分类的启发式云资源供给方法RP-CH,基于云资源共享特点,获取其他大数据分析作业的运行时监测和云资源配置信息,建立负载分类与优化云资源配置的启发式规则,并将该规则作用到贝叶斯优化算法的收益函数.基于HiBench,SparkBench测试基准的结果显示:RP-CH相对于已有方法 CherryPick、大数据分析作业的性能平均提升了58%,成本平均减少了44%.     

基于数据挖掘的新词发现

利用文本挖掘技术提出了一种用于主题式搜索引擎的专业词典库发现新专业词汇的方法,详述了如何通过关联规则挖掘来实现专业词典库的扩展,并在此基础上进行了小样本实验,实验结果显示该方法有效可行。     

DM与DSS集成的框架体系及建模

DM与DSS集成是决策环境驱动的,该文分析了DSS与DM的功能集成模式,并提出了一个DM三阶段处理过程模型(ThreeStageProcessModel),利用这个模型实现了DM系统和DSS系统的平滑结构集成,最后用面向对象的可视化建模语言(UML)对集成系统框架体系进行了建模。     

A Tree-Based Data Perturbation Approach for Privacy-Preserving Data Mining

Due to growing concerns about the privacy of personal information, organizations that use their customers' records in data mining activities are forced to take actions to protect the privacy of the individuals. A frequently used disclosure protection method is data perturbation. When used for data mining, it is desirable that perturbation preserves statistical relationships between attributes, while providing adequate protection for individual confidential data. To achieve this goal, we propose a kd-tree based perturbation method, which recursively partitions a data set into smaller subsets such that data records within each subset are more homogeneous after each partition. The confidential data in each final subset are then perturbed using the subset average. An experimental study is conducted to show the effectiveness of the proposed method.     

A Visual Analytics Framework for Microblog Data Analysis at Multiple Scales of Aggregation

Real‐time microblogs can be utilized to provide situational awareness during emergency and disaster events. However, the utilization of these datasets requires the decision makers to perform their exploration and analysis across a range of data scales from local to global, while maintaining a cohesive thematic context of the transition between the different granularity levels. The exploration of different information dimensions at the varied data and human scales remains to be a non‐trivial task. To this end, we present a visual analytics situational awareness environment that supports the real‐time exploration of microblog data across multiple scales of analysis. We classify microblogs based on a fine‐grained, crisis‐related categorization approach, and visualize the spatiotemporal evolution of multiple categories by coupling a spatial lens with a glyph‐based visual design. We propose a transparency‐based spatial context preserving technique that maintains a smooth transition between different spatial scales. To evaluate our system, we conduct user studies and provide domain expert feedback.     

基于数据挖掘的学生成绩查询系统

开发基于ASP.NET的学生成绩挖掘系统。一方面使学校教学资源得到优化,减轻教师对于学生成绩查询的工作量。另一方面,通过数据挖掘技术进行系统开发,使大量的数据能得到有效利用。挖掘出的隐藏规则对学校各方面工作的指导以及学生的培养有着重要意义。     

基于数据挖掘的恶意行为检测方法

1.引言入侵检测系统(Intrusion Detection System,IDS)是一种动态的网络攻击检测技术,能够在网络系统运行过程中发现入侵者的恶意行为和踪迹,并适时地作出反应。它是防火墙之后的第二道安全防线,与防火墙相辅相成,构成了一个完整的网络安全防护系统。     

基于数据挖掘的客户细分框架模型

数据挖掘技术在客户关系管理领域的应用较广泛,能提高客户细分能力。针对目前客户细分研究缺乏统一研究框架的问题,分析现有的客户关系管理系统构架及其与客户细分的集成关系,对客户细分问题进行构架性研究,提出一种基于数据挖掘的客广细分框架模型,包括空间逻辑模型和数据-功能-方法模型。     

基于数据挖掘的中医药数据预处理方法

总结了嵌入式软件模拟测试平台研制过程中所遇到的技术难点和相应的解决途径。该平台主要包括静态分析和动态模拟运行两个子系统，静态分析部分拥有逆向生成程序流程图的功能。介绍了生成“流程图图块文件”的思想。CPU模拟是该平台的核心，给出了CPU模拟的工作方式和流程，以及测试覆盖率的统计方案。采用了两种存储器体系结构，寻址方式的模拟不仅保证了正确地确定操作数，而且能够正确统计指令执行时间。介绍了模拟时钟的设计思想，以及中断控制模拟器的工作原理，描述了中断信号的发生、中断优先级的控制措施。     

Adaptive Intrusion Detection: A Data Mining Approach

In this paper we describe a data mining framework for constructingintrusion detection models. The first key idea is to mine system auditdata for consistent and useful patterns of program and user behavior.The other is to use the set of relevant system features presented inthe patterns to compute inductively learned classifiers that canrecognize anomalies and known intrusions. In order for the classifiersto be effective intrusion detection models, we need to have sufficientaudit data for training and also select a set of predictive systemfeatures. We propose to use the association rules and frequentepisodes computed from audit data as the basis for guiding the auditdata gathering and feature selection processes. We modify these twobasic algorithms to use axis attribute(s) and referenceattribute(s) as forms of item constraints to compute only therelevant patterns. In addition, we use an iterative level-wiseapproximate mining procedure to uncover the low frequency butimportant patterns. We use meta-learning as a mechanism to makeintrusion detection models more effective and adaptive. We report ourextensive experiments in using our framework on real-world audit data.     

基于数据挖掘的中医药数据预处理方法

中药文化的地区差异带来了中医药数据的众多不确定性，为解决基于数据挖掘的新药研制决策支持系统的数据问题，提出了一套规范原始中医药数据的处理方法。应用了数据归约技术、聚类的方法、模糊集理论改进了中医药数据的质量，使得在预处理后的中药方剂数据库中成功挖掘出重要规则，为研制中药新药提供了有力的决策支持。     

A Multimedia Data Mining Framework: Mining Information from Traffic Video Sequences

The analysis and mining of traffic video sequences to discover important but previously unknown knowledge such as vehicle identification, traffic flow, queue detection, incident detection, and the spatio-temporal relations of the vehicles at intersections, provide an economic approach for daily traffic monitoring operations. To meet such demands, a multimedia data mining framework is proposed in this paper. The proposed multimedia data mining framework analyzes the traffic video sequences using background subtraction, image/video segmentation, vehicle tracking, and modeling with the multimedia augmented transition network (MATN) model and multimedia input strings, in the domain of traffic monitoring over traffic intersections. The spatio-temporal relationships of the vehicle objects in each frame are discovered and accurately captured and modeled. Such an additional level of sophistication enabled by the proposed multimedia data mining framework in terms of spatio-temporal tracking generates a capability for automation. This capability alone can significantly influence and enhance current data processing and implementation strategies for several problems vis-à-vis traffic operations. Three real-life traffic video sequences obtained from different sources and with different weather conditions are used to illustrate the effectiveness and robustness of the proposed multimedia data mining framework by demonstrating how the proposed framework can be applied to traffic applications to answer the spatio-temporal queries.     

基于WEKA的高校学生综合测评数据挖掘

该文利用开源软件WEKA作为数据挖掘平台,对某高校的学生综合测评数据进行了数据挖掘实验,发现了很多关联规则和数据背后的隐含信息,为高校发现学生群体、行为间的关联和规律,进一步完善测评体系和素质教育提供了支持。