DATA ANALYTICS

 

 

 

 

 

 

 

DATA ANALYTICS

 

 

 

 

 

 

 

 

 

1) Big data analytics tests huge amount of data to uncover hidden patterns, correlations and other insights. With today’s technology, it’s possible to research your data and obtain answers from it soon – an attempt that’s slower and fewer efficient with more traditional business intelligence solutions.

Big data analytics is the use of advanced techniques against very large, diverse big data sets that include structured, semi-structured and unstructured data, from different resources, and in several sizes from terabytes to zettabytes. It are often defined as data sets whose size or type is beyond the power of traditional relational databases to capture, manage and process the info with low latency. Characteristics of massive data include high volume, high velocity and high variety. Sources of knowledge are getting more complex than those for traditional data because they're being driven by AI (AI), mobile devices, social media and therefore the Internet of Things (IoT).  (IBM, n.d.)

2) Common business Problems addressed by Big data analytics are as given below (Kumar, 2018)

ü  Synchronization Across Disparate Data Sources is required

ü  Acute Shortage of execs for one who Understand Big Data Analysis

ü  Getting Meaningful Insights through utilization of massive Data Analytics

ü  Uncertainty of knowledge Management Landscape

ü  Data Storage And Quality

ü  Security And Privacy of knowledge

3) A linear optimization model consists of a target function and a class of constraints within the sort of an arrangement of equations or inequalities. Optimization models are used extensively in most areas of decision-making, like financial portfolio selection as well as engineering. The site presents a structured process for optimization problem formulation, design of best possible strategy, and quality-control tools including validation, and post-solution activities.

(Britannica, n.d.)

An Example for instance a number of the essential features of LP, we start with an easy two-dimensional example. In modeling this instance, we'll review the four basic steps within the development of an LP model:

·         Identify and label the choice variables.

·         Determine the target and use the choice variables to write down an expression for the target function as a linear function of the choice variables.

·         Determine the specific constraints and write a functional expression for every of them as either a equation or a linear inequality within the decision variables.

·         Determine the implicit constraints, and write each as either a equation or a linear inequality within the decision variables.

4) In most studies, the LP problem is vastly oversimplified when first defined. For instance, consider the matter Where, the X's are defined as alternative production processes while the constraints (Ax - b) are mentioned as resource limitations. (Bruce A. McCarl and Thomas H. Spreen, 1997)

Types of Constraints

Some of the constraint types include:

ü  Resource limitations

ü  Minimum requirements

ü  Supply-demand balances, etc.

Resource Limitations

Resource limitations explain relationships between endogenous resource usage and exogenous endowments. A resource limitation restricts endogenous resource use to be adequate to an exogenous resource endowment. This constraint requires the sum of resources utilized in producing X, which uses 3 resource units per unit, plus those utilized in producing X2, which uses 4 resource units per unit, to be no greater than an exogenous resource endowment of seven units.

Minimum Requirements

Minimum requirement constraints require an endogenously determined quantity to be greater than or adequate to an exogenously specified value.

In this case the endogenous sum of X1 plus twice X2 is constrained to be greater than or adequate to the exogenously imposed requirement of 4.

Supply and Demand Balance

The supply-demand balance requires that endogenous supply should be balanced with endogenous demand and is one main constraint.

 

 

 

 

 

References

 

IBM. (n.d.). Big Data Analytics.

https://www.ibm.com/analytics/hadoop/big-data-analytics

Kumar, A. (2018, June 8). 7 Top Big Data Analytics Challenges Faced By Business Enterprises. Https://Elearningindustry.Com/Elearning-Authors/Ashish-Kumar-3.

Bruce A. McCarl and Thomas H. Spreen, 1997

 

 

 

 

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