Data Mining Practical Machine Learning Tools and Techniques

การวจยเชงคณภาพ เปนเครองมอสำคญอยางหนงสำหรบทำความเขาใจสงคมและพฤตกรรมมนษย การวจยแบบการสรางทฤษฎจากขอมล กเปนหนงในหลายระเบยบวธการวจยเชงคณภาพทกำลงไดรบความสนใจ และเปนทนยมเพมสงขนเรอยๆ จากนกวชาการ และนกวจยในสาขาสงคมศาสตร และศาสตรอนๆ เชน พฤตกรรมศาสตร สงคมวทยา สาธารณสขศาสตร พยาบาลศาสตร จตวทยาสงคม ศกษาศาสตร รฐศาสตร และสารสนเทศศกษา ดงนน หนงสอเรอง “ConstructingGrounded Theory: A Practical Guide through Qualitative Analysis” หรอ “การสรางทฤษฎจากขอมล:แนวทางการปฏบตผานการวเคราะหเชงคณภาพ” จะชวยใหผอานมความรความเขาใจถงพฒนาการของปฏบตการวจยแบบสรางทฤษฎจากขอมล ตลอดจนแนวทาง และกระบวนการปฏบตการวจยอยางเปนระบบ จงเปนหนงสอทควรคาแกการอานโดยเฉพาะนกวจยรนใหม เพอเปนแนวทางในการนำความรความเขาใจไประยกตในงานวจยของตน อกทงนกวจยผเชยวชาญสามารถอานเพอขยายมโนทศนดานวจยใหกวางขวางขน

Constructing Grounded Theory: A Practical Guide through Qualitative Analysis

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

/pdf/context-aware-computing-for-the-internet-of-things-a-survey-3nibvvc921.pdf

Context Aware Computing for The Internet of Things: A Survey

http://homepage.cs.latrobe.edu.au/sloke/papers/1-s2.0-S0167739X12001318-main.pdf

Mobile cloud computing

In this paper we describe a novel technique for detecting salient regions in an image. The detector is a generalization to affine invariance of the method introduced by Kadir and Brady [10]. The detector deems a region salient if it exhibits unpredictability in both its attributes and its spatial scale.

An affine invariant salient region detector

The semantics of hardware description languages can be represented in higher order logic. This provides a formal definition that is suitable for machine processing. Experiments are in progress at Cambridge to see whether this method can be the basis of practical tools based on the HOL theorem-proving assistant. Three languages are being investigated: ELLA, Silage and VHDL. The approaches taken for these languages are compared and current progress on building semantically-based theorem-proving tools is discussed.

/pdf/experience-with-embedding-hardware-description-languages-in-4p6muubxuh.pdf

Experience with Embedding Hardware Description Languages in HOL

Pervasive computing has emerged as a viable solution capable of providing technology-driven assistive living for elderly. The pervasive healthcare system, Context-Aware Real-time Assistant (CARA), is designed to provide personalized healthcare services for elderly in a timely and appropriate manner by adapting the healthcare technology to fit in with normal activities of the elderly and working practices of the caregivers. The work in this paper introduces a personalized, flexible, and extensible hybrid reasoning framework for CARA system in a smart home environment which provides context-aware sensor data fusion as well as anomaly detection mechanisms that supports activity of daily living analysis and alert generation. We study how the incorporation of rule-based and case-based reasoning enables CARA to become more robust and to adapt to a changing environment by continuously retraining with new cases. Noteworthy about the work is the use of case-based reasoning to detect conditional anomalies for home automation, and the use of hierarchical fuzzy rule-based reasoning to deal with exceptions and to achieve query-sensitive case retrieval and case adaptation. Case study for evaluation of this hybrid reasoning framework is carried out under simulated but realistic smart home scenarios. The results indicate the feasibility of the framework for effective at-home monitoring.

/pdf/context-aware-hybrid-reasoning-framework-for-pervasive-ihlnh887wi.pdf

Context-aware hybrid reasoning framework for pervasive healthcare

Pervasive environments generate large quantities of data, originating from backend servers, portable devices, and wireless mobile sensors. Pervasive sensing devices that monitor properties of the environment (including human beings) can be a large data source. Unprocessed datasets may include data that is faulty and irrelevant, and data that is important and useful. If not managed correctly the large amount of data from a data-rich pervasive environment may result in information overload or delivery of incorrect information.Context-sensitive quality data management aims to gather, verify, process, and manage the multiple data sources in a pervasive environment in order to deliver high quality, relevant information to the end-user. Managing the quality of data from different sources, correlating related data, and making use of context, are all essential in providing end users with accurate and meaningful data in real time. This requirement is especially true for critical applications such as in a medical environment.This article presents the Data Management System (DMS) architecture. It is designed to deliver quality data service to its users. The DMS architecture employs an agent-based middleware to intelligently and effectively manage all pervasive data sources, and to make use of context to deliver relevant information to the end-user. Two of the DMS components are presented: (1) data validation and (2) data consistency. The DMS components have been rigorously evaluated using various medical-based test cases.This article demonstrates a careful, precise approach to data based on the quality of the data and the context of its use. It emphasises the DMS architecture and the role of software agents in providing quality data management.

Data Management within mHealth Environments: Patient Sensors, Mobile Devices, and Databases

The goal of the CARA (Context-Aware Real-time Assistant) healthcare architecture is to enable improved healthcare through the intelligent use of wireless remote monitoring of patient vital signs, supplemented by rich contextual information. One of its applications currently being deployed is the remote live monitoring of a patient by a healthcare professional. The vital signs are monitored using a wireless BAN based on sensors that can monitor position in space, ECG, blood pressure, and blood oxygenation. A design goal of ubiquitous access means that all communications are performed using recent web technologies, thereby minimizing issues with firewalls and facilitating remote ease of access. The only tool required for this application is a web browser with the commonly-available Adobe Flash plug-in installed. Thus remote monitoring, independent of geographic location, is possible form any computer or suitable smartphone. Important aspects of this application include: inter-visibility between patient and caregiver; real-time interactive medical consultation; and replay, review and annotation of the remote consultation by the medical professional. The annotation of significant parts of the multi-modal monitored signals by the medical professional provides the basis for the automated intelligent analysis of the CARA system. The paper discusses the application in the context of the overall CARA healthcare architecture, and presents results of some experiments using the application.

/pdf/web-based-real-time-remote-monitoring-for-pervasive-1p0ts4sp3w.pdf

John Herbert

Papers

Experience with Embedding Hardware Description Languages in HOL

Context-aware hybrid reasoning framework for pervasive healthcare

Data Management within mHealth Environments: Patient Sensors, Mobile Devices, and Databases

Web-based real-time remote monitoring for pervasive healthcare

Fuzzy CARA - A Fuzzy-Based Context Reasoning System For Pervasive Healthcare