Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built.
Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive.
Manning Big Data Pdf Download
Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases.
Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing.
These systems leverage historical data and apply data analytics to forecast future demand. More specifically, effective inventory management software can process vast quantities of your past sales data and anticipate future demand for your inventory by factoring in lead times and seasonality.
A good inventory management system can factor in these lead times along with existing sales data to calculate your safety stock and recommend reorder points for each item. Reorder points will inform when stock should be replenished, so that you have enough to fulfill demand.
Your ecommerce business data can improve your order fulfillment speed. Many systems will enable automated shipping rules. For example, once an order is received, it can be automatically assigned to the warehouse closest to its destination, speeding up delivery and reducing shipping costs.
Ultimately, by applying intelligence to big data, these systems can recommend stock movements within the warehouse so the flow of goods is constantly optimized to minimize the time it takes for staff to pick and pack goods.
More specifically, this data can allow you to identify poor-performing shipping providers or even items more susceptible to damage, so that you can act to avoid customers repeatedly becoming dissatisfied.
You can use lean initiatives to manage your inventory, identifying and eliminating waste in the form of the costs accrued from holding inventory. This does, however, rely on having real-time inventory data visibility so you can specifically forecast demand and determine safety stock levels and reorder points.
This complete overview demonstrates how big data is transforming inventory management capabilities, reducing costs, improving operational efficiency, maximizing sales, increasing customer satisfaction, and reducing inventory shrinkage.
The What Works Centre for Crime Reduction (College of Policing UK 2017), which adopts the EMMIE rating scale, shows that the available economic evidence from prevention studies is either non existent or inadequate across a wide range of crime intervention types. One possible reason for the lack of economic evidence is the difficulty in using current economic tools where users need to input, manually, a considerable amount of point-in-time data in order to provide a range of economic estimates to inform decisions about the allocation of finite government resources. This process is time-consuming and also introduces potential input errors as human users become fatigued and/or complacent. Further, users are faced with the complication of estimating projected costs in different jurisdictions and environments and of relying on information which may be out of date. These factors are important when estimating the costs associated with interventions across different locations involving potential contextual variation. Currently, only the MCBT is capable of estimating such costs across environments, but the tool is limited to operating on expert opinion based on experience and subjective judgement.
While the use of analogous estimation and expert judgement in the MCBT allows evaluators to estimate costs (i.e. direct, indirect and intangible) based on variations in given contextual factors, challenges faced by evaluators include: (1) the constraint in incorporating additional variables into the estimation because of the limitations of Microsoft Excel (the underlying modelling tool); (2) the use of expert judgement which may lead to biased results (likely to be more conservative results as the tool advises the application of an optimism bias adjustment when using expert judgement); (3) the restriction on the use of multiple data sources; and (4) the lack of insights which could be gained from the data over time.
In order to model the costs and benefits associated with gating a community, data could be drawn from several sources (e.g. independently audited cost data, formal service delivery contract costs, security management costs, costs developed from ready reckoners and uncorroborated expert judgement) and in different forms (market values such as salary and equipment costs or non-market values such as sense of security) to define the scope (i.e. degree of inclusion of costs and benefits of relevant stakeholders) and depth (i.e. estimation of tangible and intangible costs/benefits) of the analysis (see Manning et al. 2016a, b).
Returning to our earlier example of gated communities, we propose a list of some (but not all) relevant variables that would be required in determining the cost associated with such an intervention. In Table 3 we present four predictors (independent variables or IVs) that may affect the cost associated with gating a vulnerable community (dependent variable or DV). Drawing on data from multiple inputs, evaluators, for example, may have data on the number of burglaries that have occurred in each community under review. The Smart MCBT will gather and store these data for subsequent future analyses and current estimations.
The integration of ML techniques in the Smart MCBT is designed to achieve two main goals, namely: (a) to provide input support to the user by predicting missing values, identifying potentially erroneous values, and making suggestions about relevant contextual factors; and (b) to improve the analytical capabilities of CBA by usefully reducing the number and types of variables to minimize user effort (e.g. time-consuming data entry) and develop better estimates (e.g. cost savings; crimes avoided), based on what the system learns from previous similar projects. We now turn our attention to each of these techniques to elaborate how they are deployed in the design of the Smart MCBT.
Multivariate imputation (MI) is a method for estimating incomplete data based on predictions derived from observations in the dataset (Rubin 1996; Allison 2000). This technique has been recently developed using an approach based on chained equations, or Multiple Imputation by Chained Equations (MICE) (van Buuren and Groothuis-Oudshoorn 2011). However, as Allison (2000) argues, one of the key assumptions of MI is that the data are missing at random (MAR), such that the probability of missing values in a variable Y depend only on the information contained in other variables and not Y itself. This issue is compounded when missing values exceed a certain percentage of observations, whereby MAR can no longer be reasonably assumed. Indeed, violations of the MAR can be expected in many real-world cases (Schafer and Graham 2002), although fortunately such violations have not been found to seriously bias parameter estimates for missing data (Collins et al. 2001). Nevertheless, to further address issues that can arise due to the assumptions and statistical properties of standard techniques for imputation of missing data, we propose to supplement and potentially enhance these with newer machine learning (ML) approaches.
Studies suggest that ML approaches to missing data perform on par with, and increasingly outperform, MI methods such as MICE. Richman et al. (2007) compared supervised ML algorithms with standard imputation techniques and found that support vector machines (SVM) and neural networks had the lowest error rate, and are particularly suited to scenarios where a large percentage of missing data is present. Similarly, Schmitt et al. (2015) found that data imputation through bayesian principal component analysis and fuzzy K-means outperform more standard and popular methods, notably lower error rates than multiple imputation using the MICE approach. Recent developments in nonparametric missing value imputation using a random forest approach, or MissForest (Stekhoven and Bühlmann 2012), have also been found to outperform standard methods, including MICE (Waljee et al. 2013), although the computational cost is considerably higher. As MI and ML have different strengths and limitations we propose that the Smart MCBT integrate both these methods of dealing with missing data.
The third task of User Input Support, correlation detection, can be performed on the input data to identify variables that are collinear, or more generally to identify multicollinearity in the input data. This is useful feedback to the user because it will reveal whether, and to what extent, they are entering variables that are basically highly inter-correlated and contain similar information, or are even the same variable and simply duplicated. Smart MCBT becomes even more useful as these kinds of patterns are identified across multiple similar interventions, which might suggest a re-evaluation of the input data or costs associated with the intervention. 2ff7e9595c
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