Data Mining Practical Machine Learning Tools and Techniques

1. Interaction. The interactions learners have with each other build interpersonal skills, such as listening, politely interrupting, expressing ideas, raising questions, disagreeing, paraphrasing, negotiating, and asking for help. 2. Interdependence. Learners must depend on one another to accomplish a common objective. Each group member has specific tasks to complete, and successful completion of each member’s tasks results in attaining the overall group objective.

将“Cooperative Learning”融入课堂——浅谈英语素质教育

The Web holds valuable, vast, and unstructured information about public opinion. Here, the history, current use, and future of opinion mining and sentiment analysis are discussed, along with relevant techniques and tools.

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New Avenues in Opinion Mining and Sentiment Analysis

The Long Tail: How Technology is turning mass markets into millions of niches. (p. 15). This passage from The Long Tail, pretty much sums it all up. The Long Tail by Chris Anderson is a good and worthwhile read for information scientists, computer scientists, ecommerce researchers, and others interested in all areas of Web research. The central premise of the book is that the combination of (1) the Pareto or Zipf distribution (i.e., power law probability distribution) that is characteristic of Web traffic and (2) the direct access to consumers via Web technology has opened up new business opportunities in the ''long tail''. Producers and advertisers no longer have to target ''the big hits'' at the head of the distribution. Instead, they can target the small, niche communities or even individuals in the tail of the distribution. The long tail is has been studied by Web researchers and has been noted in term usage on search engines, access times to servers, and popularity of Web sites. Andersen points out that the long tail also applies to products sold on the Web. He recounts that a sizeable percentage of Amazon sales come from books that only sell a few copies, a large number of songs from Rhapsody get downloaded only once in a month, and a significant number of movies from Netflix only get ordered occasionally. However, since the storage is in digital form for the songs and music (and Amazon out sources the storage of books) there is little additional inventory cost of these items. This phenomenon across all Web companies has led to a broadening of participation by both producers and consumers that would not have happened without the Web. The idea of the long tail is well known, of course. What Anderson has done is present it in an interesting manner and in a Web ecommerce setting. He applies it to Web businesses and then relates the multitude of other factors ongoing that permit the actual implementation of the long tail effect. Anderson also expands on prior work on the long tail by introducing an element of time, given the distribution a three dimensional effect. All in all, it is a nifty idea. The book is comprised of 14 chapters, plus an Introduction. Chapter 1 presents an overview of what the long tail is. Chapter 2 discusses the ''head'', which is the top of the tail where the …

The Long Tail: Why the Future of Business is Selling Less of More

Social big data

Ageing populations and the increase in chronic diseases all over the world demand efficient healthcare solutions for maintaining well-being of people. One strategy that has drawn significant research attention is a focus on remote health monitoring systems based on Internet of Things (IoT) technology. This concept can help decrease pressure on hospital systems and healthcare providers, reduce healthcare costs, and improve homecare especially for patients with chronic diseases and the elderly. This paper explores the use of IoT-based applications in medical field and proposes an IoT Tiered Architecture (IoTTA) towards an approach for transforming sensor data into real-time clinical feedback. This approach considers a range of aspects including sensing, sending, processing, storing, and mining and learning. Using this approach will help to develop useful and effective solutions for pursuing systems development in IoT healthcare applications. The result of the review found that the growth of IoT applications for healthcare is in areas of self-care, data mining, and machine learning.

A review on IoT healthcare monitoring applications and a vision for transforming sensor data into real-time clinical feedback

Efficient and cost effective ways of irrigation have emerged as the need of the hour due to limited sweet water resources, especially the countries that are seriously hit by a lack of sweet water reservoirs. The majority of the water is wasted due to inefficient ways of watering plants. In this paper, we propose an intelligent approach for efficient plant irrigation that has a database of daily water needs of a type of plant and decides the amount of water for a plant type on the basis of the current moisture in soil, humidity, and time of the day. This approach not only saves sweet water by efficient utilization, but also supports smart consumption of energy. Our approach employs IoT and a set of sensors to efficiently record plant data and their watering needs and the approach is implemented with a mobile phone application interface that is used to continuously monitor and control the efficient watering system. The results of this study are easy to reproduce as the sensors used are cheap and easy to access. The study discusses in this paper is experimented on small area (such as tunnel farm) but the results of the experiments show that the used approach can be generalized and can be used for large size fields for efficient irrigation. The results of the experiments also outperform the manual approach and the similar approaches for sensor based irrigation systems.

https://www.mdpi.com/1996-1073/11/12/3427/pdf

Design and Implementation of an IoT System for Smart Energy Consumption and Smart Irrigation in Tunnel Farming

Efficient Processing of Stream Data over Persistent Data

To fulfill the increasing demand of business for the latest information, current data integration approaches are moving towards real-time updates. One important element in real-time data integration is the join of a continuous incoming data stream with a disk-based relation. In this paper we investigate a stream-based join algorithm, called mesh join (MESHJOIN), and propose an improved version called reduced MESHJOIN (R-MESHJOIN). Both algorithms tune the memory, allocating parts of the memory to key components. In MESHJOIN there is a dependency between the size of partitions in an internal queue for the stream data and the number of iterations required to bring the disk-based relation into memory. This dependency hampers the optimal distribution of memory among the join components. In particular the size of the disk-buffer varies with the size of the disk-based relation which is unnecessary. On the other hand the R-MESHJOIN algorithm removes this dependency. This enables an optimal distribution of available memory among the join components. In R-MESHJOIN a change in the size of the disk-based relation does not affect the size of the disk-buffer. An experimental study is conducted in order to validate the arguments.

R-MESHJOIN for near-real-time data warehousing

An important component of near-real-time data warehouses is the near-real-time integration layer. One important element in near-real-time data integration is the join of a continuous input data stream with a disk-based relation. For high-throughput streams, stream-based algorithms, such as Mesh Join MESHJOIN, can be used. However, in MESHJOIN the performance of the algorithm is inversely proportional to the size of disk-based relation. The Index Nested Loop Join INLJ can be set up so that it processes stream input, and can deal with intermittences in the update stream but it has low throughput. This paper introduces a robust stream-based join algorithm called Hybrid Join HYBRIDJOIN, which combines the two approaches. A theoretical result shows that HYBRIDJOIN is asymptotically as fast as the fastest of both algorithms. The authors present performance measurements of the implementation. In experiments using synthetic data based on a Zipfian distribution, HYBRIDJOIN performs significantly better for typical parameters of the Zipfian distribution, and in general performs in accordance with the theoretical model while the other two algorithms are unacceptably slow under different settings.

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M. Asif Naeem

Papers

A review on IoT healthcare monitoring applications and a vision for transforming sensor data into real-time clinical feedback

Design and Implementation of an IoT System for Smart Energy Consumption and Smart Irrigation in Tunnel Farming

Efficient Processing of Stream Data over Persistent Data

R-MESHJOIN for near-real-time data warehousing

HYBRIDJOIN for Near-Real-Time Data Warehousing