Thesis (MTech (Business Information Systems))--Cape Peninsula University of Technology, 2018. / Micro-enterprises play a vital role towards the South Africa’s economic growth by contributing towards job creation. Despite the importance of the role of micro-enterprises, micro-enterprises face several challenges such as lack of finance, lack of skilled workers and lack of technological resources. Previous studies indicate that Information Technology has a distinct role in assisting micro-enterprises overcome several challenges. It is further evidenced in the extant literature that cloud computing, provides a low cost entry for enterprises to support several facets of their business operations. In the current era cloud computing requires a constant as well as fast internet connection and the South African government has various interventions to address the infrastructure divide. However, we have a scant understanding of the adoption challenges amongst micro-enterprises to adopt cloud solutions, which to date feature more prominently amongst larger organisations. This research investigated the factors that influence cloud computing adoption in the micro-enterprise sector in Cape Town. This is a city that promotes the contribution of micro-enterprises to their economic activity, and was such an ideal location to investigate cloud computing adoption amongst the micro-enterprise sector. This research provides a rich understanding of the factors that influence micro-enterprises in Cape Town to adopt cloud computing services and proposes guidelines to encourage micro-enterprises in Cape Town to use cloud services to improve their productivity. The researcher uses the UTAUT model as a framework and a qualitative research methodology to investigate the research question. Data for this research study was collected via face to face interviews with semi-structured questions of ten micro-enterprises and an IT expert. The findings showed that the factors influencing the adoption of cloud computing services are performance expectancy, effort expectancy, social influence, facilitating conditions, lack of training, cost efficiency and reduction of working hours.

Résumé indisponible / Medical data management has become a real challenge due to the emergence of new imaging technologies providing high image resolutions.This thesis focuses in particular on the management of DICOM files. DICOM is one of the most important medical standards. DICOM files have special data format where one file may contain regular data, multimedia data and services. These files are extremely heterogeneous (the schema of a file cannot be predicted) and have large data sizes. The characteristics of DICOM files added to the requirements of medical data management in general – in term of availability and accessibility- have led us to construct our research question as follows:Is it possible to build a system that: (1) is highly available, (2) supports any medical images (different specialties, modalities and physicians’ practices), (3) enables to store extremely huge/ever increasing data, (4) provides expressive accesses and (5) is cost-effective .In order to answer this question we have built a hybrid (row-column) cloud-enabled storage system. The idea of this solution is to disperse DICOM attributes thoughtfully, depending on their characteristics, over both data layouts in a way that provides the best of row-oriented and column-oriented storage models in one system. All with exploiting the interesting features of the cloud that enables us to ensure the availability and portability of medical data. Storing data on such hybrid data layout opens the door for a second research question, how to process queries efficiently over this hybrid data storage with enabling new and more efficient query plansThe originality of our proposal comes from the fact that there is currently no system that stores data in such hybrid storage (i.e. an attribute is either on row-oriented database or on column-oriented one and a given query could interrogate both storage models at the same time) and studies query processing over it.The experimental prototypes implemented in this thesis show interesting results and opens the door for multiple optimizations and research questions.

Streaming video has strong availability requirements, while for livestreamed video low latency becomes an additional significant factor. For large-scale video streaming the streaming service must be able to scale in and out in order to conform to the interchanging demands of users. Video streaming demonstrates heavily fluctuating load, where number of viewers may increase exponentially within a few minutes. In combination with the high availability guarantees suggests that the problem is non-trivial.This thesis covers the issues of providing a cost-effective distributed live video streaming application that guarantees a seamless user experience. For instance, there are multiple channels, in the order of hundred, where each has an ever changing popularity and furthermore, users are able to watch content which was streamed for some number of hours ago. Thus, the system must both provide cached streams as well as the live-stream.In this thesis, an elasticity-providing solution for live video streaming is presented. The solution is a combination of rule-based reactive algorithm for channel distribution and a predictive method for VM instance provisioning. The results show that the algorithm, when simulating 15 channels with 80000 viewers and 50 instances, keeps underallocation of channels at less than 1% while achieving significant reduction of about 125% for channel occurrences and thereby bandwidth consumption compared to the previous channel distribution solution. As the video streaming service scales in terms of number of channels and VM instances, the reduction factor increases. / Videoströmmingstjänster har starka krav på tillgänglighet, medan för live-strömmad video blir låg latens också signifikant. För storskalig videoströmmning måste tjänsten kunna skala in och ut för att överensstämma med användarnas växlande krav. Videoströmmning visar starkt varierande belastning, där antalet tittare kan öka exponentiellt inom några minuter. I kombination med de höga tillgänglighetsgarantierna antyder att problemet inte är trivialt.Denna avhandling täcker problemen med att tillhandahålla en kostnadseffektiv distribuerad live-videoströmmningstjänst som garanterar en sömlös användarupplevelse. Till exempel finns det flera kanaler, i storleksordningen hundra, där var och en har en ständigt förändrande popularitet. Därtill tillkommer dessutom att användare har möjligheten titta på innehåll som strömmats för några timmar sedan. Således måste systemet både tillhandahålla cachade strömmar såväl som direktsändning.I denna avhandling presenteras en elasticitetslösning för live video streaming. Lösningen är en kombination av en regelbaserad reaktiv algorithm för kanaldistribution och en prediktiv metod för VM-instans allokering. Resultaten visar att algoritmen, vid en simulering med 15 kanaler, 80000 tittare och 50 instanser, klarar att hålla underallokering av kanaler lägre än 1% samtidigt som totala antalet kanalinstanser reduceras med ungefär 125% jämfört med den tidigare kanaldistributionslösningen. Allteftersom videostreamingstjänsten skalar i antal kanaler och VM-instanser ökar reduktionsfaktorn ytterligare.

隨著網際網路越來越發達，資料量的膨脹速度也超乎想像，因此資料採礦的技術已是不可或缺的，而雲端運算產業也將是未來的趨勢。本研究以雲端運算的概念，透過VBA（Visual basic for application）、軟體R以及Excel的增益集-RExcel，建構一個資料採礦分析之預測（Forecasting）系統的介面。考慮將本研究所建的系統大眾化，即便使用者未具備足夠的統計知識，也可以簡易的操作本系統。因此本研究建構使用簡單且方便的資料採礦之預測（Forecasting）系統。 本研究的資料採礦之預測系統裡面包含了五種資料採礦分析的預測方法，包括了迴歸分析（Regression analysis）、分類迴歸樹（C & R tree）、時間序列分析（Time series analysis）、類神經網路（Neural net）以及羅吉斯迴歸分析（Logistic regression analysis），其中本系統的羅吉斯迴歸分析又細分為兩種，有二元羅吉斯迴歸分析（Dichotomous logistic）和多元羅吉斯迴歸分析（Polytomous logistic）。本研究的資料採礦之預測系統操作簡易，使用者只需要選取所要預測的目標變數和欲加入建模的解釋變數即可分析，系統可以自行判斷目標變數所屬的類別對資料做後續分析。 關鍵字：資料採礦、雲端運算、預測 / As the Internet developed increasingly, the amount of data grow in unimaginable speed. Therefore, data mining techniques is essential, and cloud computing industry will be the future trend. This study which bases on the concept of cloud computing builds a data mining forecasting analysis system through VBA, the R language and add-ins of Excel –RExcel. The system which is builded in this study is convenient and easy. The user can operate the system easily, even if the user does not have sufficient statistical knowledge. Accordingly, this study constructs the simple and easy data mining analysis system of the forecasting. In this study, the data mining analysis system of the forecasting contains five methods of data mining analysis : regression analysis, classification and classification & regression tree, time series analysis, neural network and logistic regression analysis. And logistic regression analysis could be separated two parts, Dichotomous logistic and Polytomous logistic . In this study, the data mining forecasting system is easy to operate, and the user need only select the target variable and explanatory variables which they need. In addition, the system could determine which categories of target variable. Keyword：data mining、cloud computing、forecasting

21世紀是雲端運算的年代，隨著ICT軟硬體技術逐漸成熟，許多公司都加入了雲端運算的行列。然而，雲端運算真的能為企業帶來競爭優勢嗎？倘若所有企業均採用了雲端運算的技術或租用了雲端運算的服務，那麼這些企業還會有競爭優勢嗎？ 　　隨著雲端運算的成熟和普及，最後雲端運算將會如同水電一般的隨手可得，失去其獨特和不可模仿性，也將會使企業失去競爭優勢。因此，企業不能單只是使用雲端運算而已，而是要利用雲端運算來形成其創新的經營模式，才能夠確保其獲利和競爭優勢。雖然目前許多業界和學術都投入了雲端運算的研究之中，但是對於一個好的、創新的、獲利的經營模式卻仍沒有一個定論。本研究試圖找出以雲端運算為基礎的企業其創新獲利的經營模式，將透過個案研究分析兩間公司，一個是搜尋網站中市佔率世界第一的Google，另一個是社交網站中擁有最多會員和最多分享次數的Facebook。 　　Google與Facebook兩者在經營模式上都是透過「平台的整合、深化與擴張」以及「平台上夥伴的引入」來達到其創新、獨特的經營模式，並成為該產業的領導廠商。本研究將深入討論兩者如何運用它們的核心能力來進行這些佈局，也希望藉由對這兩大企業的研究結果，使得我們對於雲端運算在創新經營模式的使用上有更深入的了解，進而給予新興企業或是已成長的企業一個經營模式上的建議。

近年來資訊產業最熱門的話題，莫過於雲端運算了。不過一般的學術研究與相關報導均著墨於雲端運算的定義、可能的商業模式以及潛在的信任問題。坊間雖多有質疑雲端運算是否真正對台灣的硬體製造商，尤其是終端裝置製造商有所助益，但並沒有相關的研究發現來佐證。筆者從業於工業電腦與端點銷售系統近十年，對此影響有些許的想法，希望能夠以POS端點銷售系統製造商的角度，以某個案公司為例，來觀察雲端運算對此產業所造成的衝擊以及影響，並對於此產業之價值鏈的變化進行探討，進一步地提供個案公司因應此現象的建議產品策略。 本研究希望能夠探究，當雲端運算或雲端服務應用於端點銷售系統上，將會減低終端使用者對端點銷售系統硬體的規格要求，並且延長其硬體設備的使用年限或減緩換機的頻率，甚至採用其他更便宜的替代方案，如智慧型手機或平板電腦來取代原本的端點銷售系統。本研究建議個案公司應選擇整合更多端點銷售系統的周邊輸出入裝置，產出高度整合的多功能端點銷售系統，藉此提高端點銷售系統的產品價值；或以其他種的端點銷售系統設計方式，來提高端點銷售系統在終端使用者使用環境的彈性以及降低使用者的總體成本，才能使個案公司面對著雲端運算的衝擊，有機會可以改變其產品的定位，並避免毛利之持續下降。 / Cloud Computing is one of the most popular topic of Information Technology Industry in recent years. Most of the studies and researches are focus on the definition of Cloud Computing, the possible business opportunity and the potential moral hazard. There are many people questioning about the benefit which brought from Cloud Computing to most of hardware providers in Taiwan, still there is no academic research toward this issue. The researcher has some idea about this topic based on his working experience and would like to provide some suggestion of the product strategy to A Company. This research reveals while Cloud Computing or Cloud Service to be applied in POS industry, it would reduce the hardware specification requirement from the end user, or expand the product life of a POS system which is not beneficial to the POS manufacturer, even worse is the end user would take Smart Phone or Tablet PC as the alternative of POS system. This research suggests A Company should consider integrating more POS devices into the POS system that makes the POS system more multi-functional which could increase the product value and selling price. By using different kind of system design concept would increase the flexibility of the end user to operate the system and reduce the total cost of ownership. Thus A Company could adjust an appropriate product strategy to maintain its core competence.

<p>The aim of this thesis is to develop a flexible distributed platform designed toexecute a disease outbreaks simulator in a fast way over many types of platformsand operating systems. The architecture is realized using the Elastic ComputeCloud (EC2) supplied by Amazon and Condor as middleware among the varioustypes of OS. The second part of the report describes the realization of a webapplication that allows users to manage easily the various part of the architecture,to launch the simulations and to view some statistics of the relative results.</p>

<p>Aim: In accordance with a company‟s business model, explore and analyse alternative marketing strategies for the market introduction of an Internet-based New Service in the B2B market. This study is interesting as it explores theories within different fields of research (new services, online-services and B2B market) and analyses them together to provide alternative marketing strategies.</p><p>Method: A qualitative research approach was conducted. By deskwork, secondary data was collected and analyzed. Formal half-structured interviews were carried out with the company from which we had received an assignment in order to enable a comparison between our theoretical findings and the knowledge the company already had.</p><p>Result & Conclusions: Based on the theoretical research and analysis, alternative marketing strategies are suggested. The contribution of this study to the mission company is presented.</p><p>Suggestions for future research: This study took a theoretical approach to the study of marketing strategies for an Internet-based new service in a B2B-market. Further research should concern a comprehensive quantitative research to capture currently employed strategies and analyse their consequences.</p><p>Contribution of the thesis: This study suggests alternative marketing strategies that can contribute to the re-evaluation of the preliminary business model of the mission company which envisage launching an Internet-based service in a B2B market.</p>

Aim: In accordance with a company‟s business model, explore and analyse alternative marketing strategies for the market introduction of an Internet-based New Service in the B2B market. This study is interesting as it explores theories within different fields of research (new services, online-services and B2B market) and analyses them together to provide alternative marketing strategies. Method: A qualitative research approach was conducted. By deskwork, secondary data was collected and analyzed. Formal half-structured interviews were carried out with the company from which we had received an assignment in order to enable a comparison between our theoretical findings and the knowledge the company already had. Result &amp; Conclusions: Based on the theoretical research and analysis, alternative marketing strategies are suggested. The contribution of this study to the mission company is presented. Suggestions for future research: This study took a theoretical approach to the study of marketing strategies for an Internet-based new service in a B2B-market. Further research should concern a comprehensive quantitative research to capture currently employed strategies and analyse their consequences. Contribution of the thesis: This study suggests alternative marketing strategies that can contribute to the re-evaluation of the preliminary business model of the mission company which envisage launching an Internet-based service in a B2B market.

MapReduce 是目前最熱門的雲端技術之一，用來處理大量資料，不論資料探勘、非結構化的紀錄檔、網頁索引處理及其他需要大量資料處理的科學研究，都可透過 MapReduce 得到極佳的執行效率。MapReduce 為一分散式批次資料處理程式框架，將一個工作分解為許多較小的 map 任務以及 reduce 任務，由map 處理每個小問題，再由reduce將問題彙整，得到最終的結果。 Hadoop 是一個開放原始碼的 MapReduce 架構，並且被廣泛地應用在以大規模資料運算為主的雲端計算。Hadoop有一個非常重要的元件稱為scheduler ，是 hadoop的中樞，負責調度、指派任務和資源分配的優先順序。Scheduler的任務選擇與分配方式，將會影響 MapReduce 工作的執行效率與整個叢集的使用率，目前Hadoop預設的scheduler是將任務以先進先出(FIFO)的方式進行排程。提升MapReduce運算效能的挑戰之一為如何適當的分配Mapper 和 Reducer給雲端裡的每個節點來執行。儘管過去已經有許多改善MapReduce運算效能的研究，但是大部分的方法在實際的運作中，仍存在很多的問題，如工作節點的動態負載、data locality的問題，計算節點的異質性等等。我們發現目前Hadoop對於這些問題並沒有妥善處理，並且在相關的情況下，整體效能仍有改進空間。 我們提出Data Locality Driven Scheduler(DLDS)的方法，並實踐在 Hadoop上，試圖提高scheduler的效能。我們設計不同的實驗，比較DLDS在不同狀況下和其他的排程演算法的差異。實驗結果顯示，透過提高資料的地域性，平均可提昇10% 至 15% 的效能。 / MapReduce is programming model for processing large data set. It is typically used to do distributed computing on clusters of computers such as Cloud computing platform. Examples of bit data set include unstructured logs, web indexing, scientific data, surveillance data, etc. MapReduce is a distributed processing program framework, a computing job is broken down into many smaller Map tasks and a Reduce task.Each Map task processes a partition of the given data set and Reduce aggregates the results of Maps to produce final result. Hadoop is an open-source MapReduce architecture, and is widely used in many cloud-based services.To best utilize computing resource in a cloud server, a task scheduler is essential to assign tasks to appropriate processors as well as to prioritize resource allocation. The default scheduler of Hadoop is first-in-first-out (FIFO) scheduler which is simple but has a performance inefficiency yet to be improved. Although there have been many researches aiming to improve the performance of MapReduce platform in the past year, there still have many issues hindering the performance improvement, such as dynamic load balance, data locality, and heterogeneity of computing nodes. To improve data locality, we propose a new scheduler called Data Locality Driven Scheduler (DLDS) based on Hadoop platform. DLDS improve Hadoop's performamce by allocating Map tasks as close as possible to the data block they are to process. We evaluated the proposed DLDS against several other schedulers by simulation on an 8 nodes real Hadoop system. Experimental results show that DLDS can improve data locality by 10-15%, which results in a significant performamce improvement.