No abstract

Gaining knowledge on weather patterns, trends and the influence of their extremes on various crop production yields and quality continues to be a quest by scientists, agriculturists, and managers. Precise and timely information aids decision-making, which is widely accepted as intrinsically necessary for increased production and improved quality. Studies in this research domain, especially those related to data mining and interpretation are being carried out by the authors and their colleagues. Some of this work that relates to data definition, description, analysis, and modelling is described in this paper. This includes studies that have evaluated extreme dry/wet weather events against reported yield at different scales in general. They indicate the effects of weather extremes such as prolonged high temperatures, heavy rainfall, and severe wind gusts. Occurrences of these events are among the main weather extremes that impact on many crops worldwide. Wind gusts are difficult to anticipate due to their rapid manifestation and yet can have catastrophic effects on crops and buildings. This paper examines the use of data mining methods to reveal patterns in the weather conditions, such as time of the day, month of the year, wind direction, speed, and severity using a data set from a single location. Case study data is used to provide examples of how the methods used can elicit meaningful information and depict it in a fashion usable for management decision making. Historical weather data acquired between 2008 and 2012 has been used for this study from telemetry devices installed in a vineyard in the north of New Zealand. The results show that using data mining techniques and the local weather conditions, such as relative pressure, temperature, wind direction and speed recorded at irregular intervals, can produce new knowledge relating to wind gust patterns for vineyard management decision making. OPEN ACCESSAtmosphere 2014, 5 61

<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">The rapid growth of digitalized medical records presents new opportunities for mining terra bytes of data that may provide new information & knowledge. The knowledge discovered as such could assist medical practitioners in a myriad of ways, for example in selecting the optimal diagnostic tool from among numerous possible choices. We analyzed the radiology department records of children who had undergone a CT scan procedure at Nagasaki University Hospital in the year 2004. We employed Self Organizing Maps (SOM), an unsupervised neural network based text-mining technique for the analysis. This approach led to the identification of keywords with a significance value within the narratives of the medical records that could predict & thereby lower the number of unnecessary CT requests by clinicians. This is important because, in spite of the valuable diagnostic capacity of such procedures, the overuse of medical radiation does pose significant health risks and staggering cost especially with regard to children.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"></span></p>

ABSTRACT"Data mining" for "knowledge discovery in databases" and associated computational operations first introduced in the mid-1990 s can no longer cope with the analytical issues relating to the so-called "big data". The recent buzzword big data refers to large volumes of diverse, dynamic, complex, longitudinal and/or distributed data generated from instruments, sensors, Internet transactions, email, video, click streams, noisy, structured/unstructured and/or all other digital sources available today and in the future at speeds and on scales never seen before in human history. The big data also being described using 3 Vs, volume, variety and velocity (with an additional 4th V for "veracity" and more recently with a 5th V for "value"), requires a set of new technologies, such as high performance computing i.e., exascale, architectures (distributed or grid), algorithms (for data clustering and generating association rules), programming languages, automated and scalable software tools, to uncover hidden patterns, unknown correlations and other useful information lately referred to as "actionable knowledge" or "data products" from the massive volumes of complex raw data. In view of the above facts, the paper gives an introduction to the synergistic challenges in "data-intensive" science and "exascale" computing for resolving "big data analytics" and "data science" issues in four main disciplines namely, computer science, computational science, statistics and mathematics. For the realisation of vital identified foundational aspects of an effective cyber infrastructure, basic problems need to be addressed adequately in the respective disciplines and are outlined. Finally, the paper looks at five scientific research projects that are urgently in need of high performance computing; this is in contrast to the earlier situations where private business enterprises were the drivers of better modern and faster technologies.

The census of population and dwellings undertaken by national state institutions world over at regular time intervals, is a fantastic source of information. However, there are major challenges to overcome when transforming the census data that usually consists of a vast number of attributes, into useful knowledge. In this paper, an artificial intelligent (AI) based approach is investigated to select appropriate attribute features that indicate interesting patterns in Beppu census wards in 2000 and 2010. The results of the self-organising map or SOM (unsupervised artificial neural network) based clustering, GIS visualisation and machine learning (J48 and JRip functions of WEKA), provide relevant discerning features, new patterns and new knowledge that can be of use to many professionals, such as urban/transport planers and resources management.