ITECH7407 Real Time Analytics For Business Intelligence Tools

ess Intelligence is a cloud based, continuous investigation application that permitted in portrayal, determining, improvement and assessment of the data from high volume of organized crude information. The utilization of Watson Analytics furnished the watcher with the simple administration, joint effort and coordination of the data. Through the information examination in this venture, the Alistair Haase Pty Ltd, gone for assessing the example and current patterns seen amid the previous years. Notwithstanding that, extend likewise gone for building up an expectation demonstrate for recognizing the likelihood of the precipitation sum throughout the following twelve month (Devarakonda et al., 2014). Moreover, in light of the assessment and recognizable proof of the example, the venture gave required and pertinent proposal to the CEO of Alistair Haase Pty Ltd, for building up the operation technique and amplifying the Grain and Field Beans creation throughout the following year.

Dataset Description: 

The crude information about the precipitation changeability has been acquired from the accumulation of the information from Bureau of Meteorology, Australian Government's authentic site. The information has been acquired from the Aerodrome Ballarat Station. The informational collection got contained the information in regards to the normal precipitation recorded from 1908 January to 2017 April. The information contained different invalid esteems. Before utilizing the dataset for investigation in Watson Analytics the information was cleaned for guaranteeing high caliber of dataset (Dijk et al., 2013). The dataset contained the precipitation sum recorded and watched every month and the estimation of precipitation sum was recorded in millimeters. For the dataset, the different months were spoken to regarding numeric esteems where 1 speaks to January and 12 is utilized for speaking to December. The nature of the precipitation record were see from the Ballarat station. The information acquired were thought to be exact and dependable for deciding the investigation of the precipitation design. The point by point and understanding of Watson Analytics instrument are given in the underneath area.

Alistair Haase Pty Ltd firm works their business with the generation of Grain and Field Beans products. The creation of Grain and Field Beans requires practically zero precipitation for delivering high caliber of yields. Anwar et al., (2015) demonstrated that for the most part, Grain and Field Beans requires 700 to 1300 millimeter of precipitation amid the developing time frame for delivering advanced yields. The creation and development of the Grain and Field Beans crops requires around 110 days from introductory advancement to develop development of the arrangement (Haws et al., 2015). The underlying rise of the Grain and Field Beans plant requires least precipitation of around 0.03 to 0.20 inch every day water. The underlying stage proceeds for around one month or 25 days. After the underlying advancement, the main sprout of the Grain and Field Beans plats watched when the plant requires greatest water for roughly 0.09 to 0.36 inch day by day water. The yield advancement stage goes on for around 35 days when the harvests requires greatest water for appropriate development. Amid the middle of the season, or the most recent 50 days of development and open bubble season, the water prerequisite of the increments to greatest of 0.44 inch for each day (Heinemann et al., 2015). The water necessity of the water plant is electively low considering alternate harvests. The water system water is sufficient for guaranteeing the successful and appropriate development of the plant. Notwithstanding that, more water is destructive to the yields and harms the quality and creation (Aggarwal, and Madhukar, 2016). Along these lines the season with least and no precipitation for at least three consistent months are good to grow and generation of Grain and Field Beans yields.

The Grain and Field Beans creation required low precipitation and has low water utilization criteria for guaranteeing the high caliber of Grain and Field Beans yield. Along these lines, it is fundamental to recognize the months encountering low precipitation. From the examination, the time of January, March, February and December has been assessed having the least precipitation. The Grain and Field Beans creation requires around three months to get development. In this manner, for the Grain and Field Beans  generation, the great month distinguished are as December to February and January to March.

The month to month forecast display gave the detailed expectation of the precipitation plausibility throughout the following twelve months. From the expectation display, it has been identified that October is anticipated to have the best measure of precipitation, while the times of January to February are most reduced precipitation throughout the following twelve months.

From the above investigation, it has been distinguished that the time of the January experienced most minimal precipitation over the previous years and December is the third least month encountering low precipitation. The above figures represents the pattern of precipitation inconstancy throughout the months. The precipitation design have been assessed in light of the normal yearly precipitation and the normal precipitation seen throughout the time of December and January individually. The investigation have demonstrated the slow expire in the precipitation sum over the previous years. Notwithstanding that, it has been distinguished that the time of December has gotten bring down precipitation throughout the year in examination with the precipitation seen in January.

Likewise, the precipitation patterns of February and March has been assessed. The assurance of the precipitation design over the two months helped in showing the development time of the Grain and Field Beans plant. The middle of the season and development time of the Grain and Field Beans requires level and more precipitation contrasting and the sowing season. The sowing of the Grain and Field Beans plant amid the time of December could bring about the development time frame amid February while the sowing of the yields amid January could bring about the development time frame amid March.

The above figure exhibits the precipitation example and patterns over the previous years. From the examination it has been seen that the yearly precipitation radically changed over the previous year’s affecting the creation of Grain and Field Beans. In this manner, it is basic to decide and foresee the precipitation fluctuation throughout the following twelve months for improving the Grain and Field Beans generation.

The dashboard speak to the likelihood of the precipitation throughout the following twelve months and the noteworthy months that decide the measure of yearly precipitation over the course of the year (Chen, Argentinis, and Weber, 2016). The use of the Predictive Dashboard has given the watcher to comprehend the precipitation plausibility and the noteworthy driver of yearly precipitation. From the dashboard, it has been watched that both the prescient model has demonstrated October getting the most astounding precipitation throughout the year. Notwithstanding that, the time of January, February and March have been related to the likelihood of low precipitation. The yearly patterns dashboard has demonstrated constant change in the precipitation design throughout the years. The yearly patterns demonstrated that the measure of yearly precipitation changes in a huge level. The drivers of yearly precipitation has anticipated October getting the most astounding precipitation taken after by June.

In the view of the month to month precipitation forecast, it has been seen that the over the next year on an average, there will be 100 millimeters rain.

Recommendations to CEO

The Grain and Field Beans plant is known to endure abnormal state of dry season and water deficiency and still yield great nature of Grain and Field Beans amid the earth of low precipitation. Notwithstanding that, the generation of Grain and Field Beans should be possible on the spots getting yearly precipitation of under 500 mm (Collinset et al., 2016). Aside from that, amid the time of development, the Grain and Field Beans plant requires equitably spread higher precipitation. This permits in guaranteeing the nature of the Grain and Field Beans crops developed. Then again, the more than satisfactory precipitation amid the development time frame can brings about boll rot and decimation of the Grain and Field Beans creation (Guidi et al., 2016). The present precipitation changeability and the example has brought about the generation of low nature of Grain and Field Beans and lessened the gainfulness of the association. The examination of the crude information about the precipitation recorded in the previous years in IBM Watson Analytics have given the data and the normal for the precipitation design affecting the Grain and Field Beans  generation (Hoyt et al., 2016). From the examination and investigation of the precipitation changeability, different suggestions has been given to Alistair Haase Pty Ltd, for upgrading the Grain and Field Beans creation throughout the following year.

Elective Source of Water: The creation and development of Grain and Field Beans requires has low water necessity and could outstand the dry spell and insufficiency of water amid the sowing time frame. In this manner, Alistair Haase Pty Ltd, necessities to sow the Grain and Field Beans crops amid the period with low precipitation. In any case, then again, amid the mode-season and development of the products, Grain and Field Beans requires rise to water for yielding high caliber of harvests. From the examination, it has been watched the precipitation design definitely changes throughout the year (Gandomi, and Haider, 2015). Alistair Haase Pty Ltd, requirements to anticipate an option wellspring of water amid the mid-season and development period. Alistair Haase Pty Ltd, ought to guarantee the nonstop water system from the close-by Murray and Murrumbidgee River for guaranteeing the water to the Grain and Field Beans field (Hurley, 2015). Aside from that, the protection of the precipitation amid the overwhelming precipitation and utilizing them amid the Grain and Field Beans generation permits in enhanced utilize and reuse of the water amid the critical moment.

Time Rotation for Production: Grain and Field Beans is sub-tropical or tropical plant and can be created during the time in view of the atmosphere and barometrical condition. The great yielding of Grain and Field Beans harvests required low water amid the sowing season and little precipitation amid the development time frame. Customarily, Grain and Field Beans is developed at Australia amid the time of October to December (Imteaz, Paudel, and Gato-Trinidad, 2015). The seeds are sown amid the time of October. Yet, the present investigation on the Watson Analytics has demonstrated that October has been portrayed as the month getting most elevated precipitation. Thusly, sowing of the yields amid October decide and diminished the nature of the Grain and Field Beans generation along harm crops. The assurance of the present patterns of precipitation demonstrated that January, February, December and March has been portrayed with low measure of precipitation. There in light of the investigation, it has been prescribed to Alistair Haase Pty Ltd, to turn their development time between the time of December to walk for yielding high caliber of Grain and Field Beans.

Nonstop Monitoring of the Rainfall Trends: The investigation and late patterns of the precipitation changeability has demonstrated constant change in the precipitation design consistently. Watson Analytics has given the forecast of precipitation example and plausibility of precipitation throughout the following twelve months. The persistent change in the precipitation impacts the Grain and Field Beans generation at Alistair Haase Pty Ltd. Along these lines, Alistair Haase Pty Ltd, necessities to constantly participate in the checking and assessment of the precipitation design for amplifying the Grain and Field Beans generation.

Conclusion 

The crude information small effectively assessed and the crucial data were gotten from the examination and dashboard made through the IBM Watson Analytics. The crude information examination has given the knowledge of the example and patterns seen in the precipitation inconstancy over the different months. The time of January has been distinguished for encountering least precipitation over the previous years. Be that as it may, the following three months having the low precipitation were distinguished as February, December and March. Since Grain and Field Beans creation and development required roughly three months’ time of generation time, three continuous month with low precipitation were considered for Grain and Field Beans generation. From the examination it has been watched that the time of December is exceptionally positive for the development of Grain and Field Beans. Thusly, the day and age between December to February and January to walk has been recognized as the fitting time for improving and amplifying the generation of Grain and Field Beans. The use of the Watson Analytics apparatuses have permitted in building up the perception of the precipitation inconstancy and precipitation inclines over the Ballarat Region. Notwithstanding that, the Watson examination could give the prescient model to deciding and anticipating the likelihood of the precipitation slants throughout the following twelve months.

Reference:

Aggarwal, M., & Madhukar, M. (2016). IBM’s Watson Analytics for Health Care: A Miracle Made True. Cloud Computing Systems and Applications in Healthcare, 117-134.

Anwar, M. R., Li Liu, D., Farquharson, R., Macadam, I., Abadi, A., Finlayson, J., ... & Ramilan, T. (2015). Climate change impacts on phenology and yields of five broadacre crops at four climatologically distinct locations in Australia. Agricultural Systems, 132, 133-144.

Ashcroft, L., & Karoly, D. J. (2016, April). Understanding extreme rainfall events in Australia through historical data. In EGU General Assembly Conference Abstracts (Vol. 18, p. 266).

Challinor, A. J., Watson, J., Lobell, D. B., Howden, S. M., Smith, D. R., & Chhetri, N. (2014). A meta-analysis of crop yield under climate change and adaptation. Nature Climate Change, 4(4), 287-291.

Chen, Y., Argentinis, J.E. & Weber, G., (2016). IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research. Clinical therapeutics, 38(4), pp.688-701.

Christodoulakis, C., Kandogan, E., Terrizzano, I. G., & Miller, R. J. (2017, March). VIQS: Visual Interactive Exploration of Query Semantics. In Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics (pp. 25-32). ACM.

Chung, P. T., & Chung, S. H. (2013, May). On data integration and data mining for developing business intelligence. In Systems, Applications and Technology Conference (LISAT), 2013 IEEE Long Island (pp. 1-6). IEEE.

Collins, H., Glover, H., Myers, F., & Watson, M. (2016). Automation in distance learning: an empirical study of unlearning and academic identity change linked to automation of student messaging within distance learning.

Contreras, S., Santoni, C. S., & Jobbágy, E. G. (2013). Abrupt watercourse formation in a semiarid sedimentary landscape of central Argentina: the roles of forest clearing, rainfall variability and seismic activity. Ecohydrology, 6(5), 794-805.

Demirkan, H. & Delen, D., (2013). Leveraging the capabilities of service-oriented decision support systems: Putting analytics & big data in cloud.Decision Support Systems, 55(1), pp.412-421.

Devarakonda, M., Zhang, D., Tsou, C. H., & Bornea, M. (2014, October). Problem-oriented patient record summary: an early report on a Watson application. In e-Health Networking, Applications and Services (Healthcom), 2014 IEEE 16th International Conference on (pp. 281-286). IEEE.

Dijk, A. I., Beck, H. E., Crosbie, R. S., Jeu, R. A., Liu, Y. Y., Podger, G. M., ... & Viney, N. R. (2013). The Millennium Drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society. Water Resources Research, 49(2), 1040-1057.

Dijk, A. I., Beck, H. E., Crosbie, R. S., Jeu, R. A., Liu, Y. Y., Podger, G. M., ... & Viney, N. R. (2013). The Millennium Drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society. Water Resources Research, 49(2), 1040-1057.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.

Guidi, G., Miniati, R., Mazzola, M., & Iadanza, E. (2016). Case Study: IBM Watson Analytics Cloud Platform as Analytics-as-a-Service System for Heart Failure Early Detection. Future Internet, 8(3), 32.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.

Guidi, G., Miniati, R., Mazzola, M., & Iadanza, E. (2016). Case Study: IBM Watson Analytics Cloud Platform as Analytics-as-a-Service System for Heart Failure Early Detection. Future Internet, 8(3), 32.

Haws, D. C., Rish, I., Teyssedre, S., He, D., Lozano, A. C., Kambadur, P., ... & Parida, L. (2015). Variable-selection emerges on top in empirical comparison of whole-genome complex-trait prediction methods. PloS one, 10(10), e0138903.

Heinemann, A. B., Barrios-Perez, C., Ramirez-Villegas, J., Arango-Londoño, D., Bonilla-Findji, O., Medeiros, J. C., & Jarvis, A. (2015). Variation and impact of drought-stress patterns across upland cotton target population of environments in Brazil. Journal of experimental botany, 66(12), 3625-3638.

Hoyt, R. E., Snider, D., Thompson, C., & Mantravadi, S. (2016). IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics. JMIR Public Health and Surveillance, 2(2).

Hurley, B. (2015). TIM Lecture Series-Improving the Self-Service Customer Experience: The Case of IBM Watson and Purple Forge. Technology Innovation Management Review, 5(9), 36.

Ibm.com. (2017). IBM - Australia. [online] Available at: https://www.ibm.com/au-en/ [Accessed Accessed 10 May. 2017].

Imteaz, M. A., Paudel, U., & Gato-Trinidad, S. (2015). Climatic variability of potential rainwater savings: A case study for Adelaide. 9th International Water Sensitive Urban Design (WSUD 2015), 233.