Large scale machine learning has many characteristics that can be exploited in the system designs to improve its efficiency. This dissertation demonstrates that the characteristics of the ML computations can be exploited in the design and implementation of parameter server systems, to greatly improve the efficiency by an order of magnitude or more. We support this thesis statement with three case study systems, IterStore, GeePS, and MLtuner. IterStore is an optimized parameter server system design that exploits the repeated data access pattern characteristic of ML computations. The designed optimizations allow IterStore to reduce the total run time of our ML benchmarks by up to 50×. GeePS is a parameter server that is specialized for deep learning on distributed GPUs. By exploiting the layer-by-layer data access and computation pattern of deep learning, GeePS provides almost linear scalability from single-machine baselines (13× more training throughput with 16 machines), and also supports neural networks that do not fit in GPU memory. MLtuner is a system for automatically tuning the training tunables of ML tasks. It exploits the characteristic that the best tunable settings can often be decided quickly with just a short trial time. By making use of optimization-guided online trial-and-error, MLtuner can robustly find and re-tune tunable settings for a variety of machine learning applications, including image classification, video classification, and matrix factorization, and is over an order of magnitude faster than traditional hyperparameter tuning approaches.

No / Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders.

Background: The implementation of Big Data Analytics (BDA) has drastically increased within several sectors such as retailing. Due to its rapidly altering environment, companies have to adapt and modify their business strategies and models accordingly. The concepts of ambidexterity and agility are said to act as mediators to these changes in relation to a company’s capabilities within BDA. Problem: Research within the respective fields of dynamic mediators and BDAC have been conducted, but the investigation of specific traits of these mediators, their interconnection and its impact on BDAC is scant. This actuality is seen as a surprise from scholars, calling for further empirical investigation.  Purpose: This paper sought to empirically investigate what specific traits of ambidexterity and agility that emerged within the case company of Åhlen’s BDA-operation, and how these traits are interconnected. It further studied how these traits and their interplay impacts the firm's talent and managerial BDAC. Method: A qualitative case study on the retail firm Åhlens was conducted with three participants central to the firm's BDA-operation. Semi-structured interviews were conducted with questions derived from the conceptual framework based upon reviewed literature and pilot interviews. The data was then analyzed and matched to literature using a thematic analysis approach.  Results: Five ambidextrous traits and three agile traits were found within Åhlen’s BDA-operation. Analysis of these traits showcased a clear positive impact on Åhlen’s BDAC, when properly interconnected. Further, it was found that in absence of such interplay, the dynamic mediators did not have as positive impact and occasionally even disruptive effects on the firm’s BDAC. Hence it was concluded that proper connection between the mediators had to be present in order to successfully impact and enhance the capabilities.

Background: Big data analytics has been referred to as a hype the past decade, making manyorganizations adopt data-driven processes to stay competitive in their industries. Many of theorganizations adopting big data analytics use agile methodologies where the most importantoutcome is to maximize business value. Multiple scholars argue that big data analytics lead toincreased business value, however, there is a theoretical gap within the literature about how agileorganizations can capture this business value in a practically relevant way. Purpose: Building on a combined definition that capturing business value means being able todefine-, communicate- and measure it, the purpose of this thesis is to explore how agileorganizations capture business value from big data analytics, as well as find out what aspects ofvalue are relevant when defining it. Method: This study follows an abductive research approach by having a foundation in theorythrough the use of a qualitative research design. A single case study of Nike Inc. was conducted togenerate the primary data for this thesis where nine participants from different domains within theorganization were interviewed and the results were analysed with a thematic content analysis. Findings: The findings indicate that, in order for agile organizations to capture business valuegenerated from big data analytics, they need to (1) define the value through a synthezised valuemap, (2) establish a common language with the help of a business translator and agile methods,and (3), measure the business value before-, during- and after the development by usingindividually idenified KPIs derived from the business value definition.

The purpose of this study was (1) to identify visitors’ behaviors in and perceptions of linear parks, (2) to identify social media users’ behaviors in and perceptions of linear parks, and (3) to compare small data with big data. This chapter discusses the main findings and their implications for practitioners such as landscape architects and urban planners. It has three sections. The first addresses the main findings in the order of the research questions at the center of the study. The second describes implications and recommendations for practitioners. The final section discusses the limitations of the study and suggests directions for future work. This study compares two methods of data collection, focused on activities and benefits. The survey asked respondents to check all the activities they did in the park. Social media users’ activities were detected by term frequency in social media data. Both results ordered the activities similarly. For example social interaction and art viewing were most popular on the High Line, then the 606, then the High Bridge according to both methods. Both methods also reported that High Line visitors engaged in viewing from overlooks the most. As for benefits, according to both methods vistors to the 606 were more satisfied than High Line visitors with the parks’ social and natural benefits. These results suggest social media analytics can replace surveys when the textual information is sufficient for analysis. Social media analytics also differ from surveys in accuracy of results. For example, social media revealed that 606 users were interested in events and worried about housing prices and crimes, but the pre-designed survey could not capture those facts. Social media analytics can also catch hidden and more general information: through cluster analysis, we found possible reasons for the High Line’s success in the arts and in the New York City itself. These results involve general information that would be hard to identify through a survey. On the other hand, surveys provide specific information and can describe visitors’ demographics, motivations, travel information, and specific benefits. For example, 606 users tend to be young, high-income, well educated, white, and female. These data cannot be collected through social media. / Doctor of Philosophy / Turning unused infrastructure into green infrastructure, such as linear parks, is not a new approach to managing brownfields. In the last few decades, changes in the industrial structure and the development of transportation have had a profound effect on urban spatial structure. As the need for infrastructure, which played an important role in the development of past industry, has decreased, many industrial sites, power plants, and military bases have become unused. This study identifies new ways of collecting information about a new type of park, linear parks, using a new method, social media analytics. The results are then compared with survey results to establish the credibility of social media analytics. Lastly, shortcomings of social media analytics are identified. This study is meaningful in helping us understand the users of new types of parks and suggesting design and planning strategies. Regarding methodology, this study also involves evaluating the use of social media analytics and its advantages, disadvantages, and reliability.

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Yes / Big Data Analytics (BDA) is an emerging phenomenon with the reported potential to transform how firms manage and enhance high value businesses performance. The purpose of our study is to investigate the impact of BDA on operations management in the manufacturing sector, which is an acknowledged infrequently researched context. Using an interpretive qualitative approach, this empirical study leverages a comparative case study of three manufacturing companies with varying levels of BDA usage (experimental, moderate and heavy). The information technology (IT) business value literature and a resource based view informed the development of our research propositions and the conceptual framework that illuminated the relationships between BDA capability and organizational readiness and design. Our findings indicate that BDA capability (in terms of data sourcing, access, integration, and delivery, analytical capabilities, and people’s expertise) along with organizational readiness and design factors (such as BDA strategy, top management support, financial resources, and employee engagement) facilitated better utilization of BDA in manufacturing decision making, and thus enhanced high value business performance. Our results also highlight important managerial implications related to the impact of BDA on empowerment of employees, and how BDA can be integrated into organizations to augment rather than replace management capabilities. Our research will be of benefit to academics and practitioners in further aiding our understanding of BDA utilization in transforming operations and production management. It adds to the body of limited empirically based knowledge by highlighting the real business value resulting from applying BDA in manufacturing firms and thus encouraging beneficial economic societal changes.

Yes / Big Data (BD), with their potential to ascertain valued insights for enhanced decision-making process, have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. Therefore, prior to hasty use and buying costly BD tools, there is a need for organizations to first understand the BDA landscape.Given the significant nature of the BDand BDA, this paper presents a state-ofthe- art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/ employed by organizations to help others understand this landscape with the objective of making robust investment decisions. In doing so, systematically analysing and synthesizing the extant research published on BD and BDA area. More specifically, the authors seek to answer the following two principal questions: Q1 –What are the different types of BD challenges theorized/proposed/confronted by organizations? and Q2 – What are the different types of BDA methods theorized/proposed/employed to overcome BD challenges?. This systematic literature review (SLR) is carried out through observing and understanding the past trends and extant patterns/themes in the BDA research area, evaluating contributions, summarizing knowledge, thereby identifying limitations, implications and potential further research avenues to support the academic community in exploring research themes/patterns. Thus, to trace the implementation of BD strategies, a profiling method is employed to analyze articles (published in English-speaking peer-reviewed journals between 1996 and 2015) extracted from the Scopus database. The analysis presented in this paper has identified relevant BD research studies that have contributed both conceptually and empirically to the expansion and accrual of intellectual wealth to the BDA in technology and organizational resource management discipline.

Yes / In view of the burgeoning scholarly works on big data and big data analytical capabilities, there remains limited research on how different access to big data and different big data analytic capabilities possessed by firms can generate diverse conditions leading to business failure. To fill this gap in the existing literature, an integrated framework was developed that entailed two approaches to big data as an asset (i.e. threshold resource and distinctive resource) and two types of competences in big data analytics (i.e. threshold competence and distinctive/core competence). The analysis provides insights into how ordinary big data analytic capability and mere possession of big data are more likely to create conditions for business failure. The study extends the existing streams of research by shedding light on decisions and processes in facilitating or hampering firms’ ability to harness big data to mitigate the cause of business failures. The analysis led to the categorization of a number of fruitful avenues for research on data-driven approaches to business failure.

Big data analytics (BDA) has become an increasingly popular topic over the years amongst academics and practitioners alike. Big data, which is an important part of BDA, was originally defined with three Vs, being volume, velocity and variety. In later years more Vs have surfaced to better accommodate the current need. The analytics of BDA consists of different methods of analysing gathered data. Analysing data can provide insights to organisations which in turn can give organisations competitive advantage and enhance their businesses. Looking into the necessary resources needed to build big data analytic capabilities (BDAC), this thesis sought out to find how Swedish organisations enable and use BDA in their businesses. This thesis also investigated whether BDA could lead to performance enhancement and competitive advantage to organisations. A theoretical framework based on previous studies was adapted and used in order to help answer the thesis purpose. A qualitative study was deemed the most suitable for this study using semi-structured interviews. Previous studies in this field pointed to the fact that organisations may not be aware of how or why to use or enable BDA. According to current literature, different resources are needed to work in conjunction with each other in order to create BDAC and enable BDA to be utilized. Several different studies discuss challenges such as the culture of the organisation, human skills, and the need for top management to support BDA initiatives to succeed. The findings from the interviews in this study indicated that in a Swedish context the different resources, such as data, technical skills, and data driven culture, amongst others, are being used to enable BDA. Furthermore, the result showed that business process improvements are a first staple in organisations use of benefiting from BDA. This is because of the ease and security in calculating profits and effect from such an investment. Depending on how far an organisation have come in their transformation process they may also innovate and/or create products or services from insights made possible from BDA. / Big data analytics (BDA) har blivit ett populärt ämne under de senaste åren hos akademiker och utövare. Big data, som är en viktig del av BDA, var först definierad med tre Vs, volym, hastighet och varietet. På senare år har flertalet V framkommit för att bättre uttrycka det nuvarande behovet. Analysdelen i BDA består av olika metoder av analysering av data. Dataanalysering som görs kan ge insikter till organisationer, som i sin tur kan ge organisationer konkurrensfördelar och förbättra deras företag. Genom att definiera de resurser som krävs för att bygga big data analytic capabilities (BDAC), så försökte denna avhandling att visa hur svenska organisationer möjliggör och använder BDA i sina företag. Avhandlingen försökte också härleda om BDA kan leda till prestandaförbättringar och konkurrensfördelar för organisationer. Ett teoretiskt ramverk, baserat på tidigare studier, anpassades och användes för att hjälpa till att svara på avhandlingens syfte. En kvalitativ studie utsågs vara den mest passande ansatsen, tillsammans med semi-strukturerade intervjuer. Tidigare studier inom området visade på att organisationer kanske inte helt är medvetna om hur eller varför BDA möjliggörs eller kan användas. Enligt den nuvarande litteraturen så behöver olika resurser arbeta tillsammans med varandra för att skapa BDAC och möjliggöra att BDA kan utnyttjas till fullo. Flera olika studier diskuterade utmaningar såsom kulturen inom organisationen, kompetens hos anställda och att ledningen behöver stödja BDA initiativ för att lyckas. Fynden från studiens intervjuer indikerade, i ett svenskt sammanhang, att olika resurser såsom data, tekniska färdigheter och datadriven kultur bland annat, används för att möjliggöra BDA. Fortsättningsvis påvisade resultatet att affärsprocessförbättring är en första stapel i användandet av fördelarna från BDA. Anledningen till det är för att det är lättare och säkrare med beräkning av förtjänst och effekt från en sådan investering. Beroende på hur långt en organisation har kommit i deras transformationsprocess kan de också innovera och/eller skapa produkter eller tjänster som möjliggjorts av insikter från BDA.