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Showing posts with label model. Show all posts
Showing posts with label model. Show all posts

Sunday, March 23, 2014

MH370 flight mystery may have an answer


Data Science perspective: MH370 flight data are crucial

It has been a while and still no answer for the missing airplane of flight MH370. This case may give many possibilities, so guessing by trying each of them is painful and may lead to wrong directions, time consuming and frustrations. So simulating both physical and mathematical model maybe an answer

Mathematical Model

Data Science is newly established profession to solve problems with data insights in high volume of data, so in this case like airplane information systems, weather condition, pattern recognition, path selections etc.
Based in the data above, Data Scientists like me can build specific models to adapt exact or similar scenarios of the missing plane in the line MH370
This may give as good picture what may happen to flight, where may landed, what weather condition we had on that time, what other circumstances occurred and what impact they had over the plane.



Picture: Possible routes

If Malaysian Airport can provide with specific technical information about MH370 airplane, historical data of all MH370 flights, we may find important patterns to solve this mystery. Period between departure and lost signal may give us the distance compared to average distance of all MH370 flights on the same period. Adding weather condition may help us to segments the other MH370 flights that had same weather condition to seek specific scenarios under same weather condition. Route lines are similar to all flights in the same airline, but segmenting them to specific conditions of the missing flight like weather, airplane type, technical conditions, air pressure etc… may lead us to better targets. Technical check before departure may give us information of what have been checked or not and if there is room for possible technical failure. If there is room for failures, we can use data from all technical failure plan crashes to predict the time occurred the failure by that distance also segmented to adapt most the missing flight model.
Big Data technology provides us with the power to analyze big and complicated data sets, and there are plenty of professionals to do so.

Physical model- Simulation

I am not the expert of the field but it can be very smart to build a flight simulation of MH370 based on the mathematical model that we provided here and including other external data that have impact on the fight itself. Sometimes visualization may bring in table other factors that may be decisive in solving the mystery.
These represent alternative approaches to what may help involved institutions to solve this case and I hope they may consider these.

Tuesday, March 20, 2012

Mike Walsh, futurist

In our VK2012 was invited Mike Walsh and he had a keynote about technology future. He instisted that the future of the World is DATA and claimed the most important profession of the future will be Data Scientist. Let the future begin and let say I am a Data Scientist.

Thursday, March 15, 2012

BI for Customers

BI for everyone, does it sound familiar!

It is a fact that Business Intelligence was dedicated to big companies, enterprises because they have that amount of data to be considered interesting for analytics and BI. Now, Gartner started the idea of Bi for mid-size and small businesses, so they need attention too based on BI surveys. But, have you ever thought for a BI solution in Customer Level, or more detailed do you think you can handle a personal BI solution.
ELA will give you the answer.

ELA solution for Customer Intelligence

What ELA is actually?

Elegant Analytics represents the name of a general BI solution in or group of methodologies in Analytics that adapts to every Business profile. In this case, ELA will provide solution for personal finance and planning of your budget. The name of the product is PFI (Personnal Finance Intelligence). Inspired by the TV Show “Luksusfellen” here in Norway, this BI end-user tool may be a solution for all these who fail to maintain well their own economy and for those who want to perform their economy as well. The purpose of this project is to create a Customer Analytical Cube that would process data for each bank costumer using his/her history for its own benefit and then answer you most important queries that users do against their own data.

This solution will include also benchmarking against an Imaginary subject (Ola Nordman) that can be Min, Max or Avg of the customer’s measures in a certain region, for a period of time, similar age group, sex and income levels.

For having more controle and planning your own economy, will be an extra parameter as Target, so users (bank customers) will put their targets for costs and income a month, quarter or a year ahead and always will be warned when they are about to achieve the amount they targeted.

If you want to read more then follow the link where you can download the full project.

Friday, March 9, 2012

BIandIT.com, a website that is going to have all important things in BI

My web site is Under Construction and is going to have the hotest topics and trends in Business Intelligence. You can find elegant solutions in Business Intelligence, special calculated members, Time Intelligence, Customer Intelligence, Competition Intelligence, Market Intelligence, Spatial Intelligence and Predictive Analytics. The site will have also Office Templates for Business Start-ups, for Market Analysis, ROI, First Year Costs etc... www.biandit.com is comming SOON :)

How I do Predictive Analytics without Data Mining?

This hot topic is comming soon. I will just describe a bit what will this topic will include. This topic is going to show how to do Predictive Analytics on your data without using Data Mining or DMX and just using MDX. How can Data Mining prediction Algorithms be "translated" from DMX to MDX. How accurate are they and what is the benefit of using MDX?

Tuesday, November 9, 2010

Information Managment Concepts

Following the behavioral science theory of management, mainly developed at Carnegie Mellon University and prominently represented by Barnard, Richard M. Cyert, March and Simon, most of what goes on in service organizations is actually decision making and information processes. The crucial factor in the information and decision process analysis is thus individuals’ limited ability to process information and to make decisions under these limitations.

According to March and Simon [1], organizations have to be considered as cooperative systems with a high level of information processing and a vast need for decision making at various levels. They also claimed that there are factors that would prevent individuals from acting strictly rational, in opposite to what has been proposed and advocated by classic theorists

Instead of using the model of the economic man, as advocated in classic theory, they proposed the administrative man as an alternative based on their argumentation about the cognitive limits of rationality.

While the theories developed at Carnegie Mellon clearly filled some theoretical gaps in the discipline, March and Simon [1] did not propose a certain organizational form that they considered especially feasible for coping with cognitive limitations and bounded rationality of decision-makers. Through their own argumentation against normative decision-making models, i.e., models that prescribe people how they ought to choose, they also abandoned the idea of an ideal organizational form.

In addition to the factors mentioned by March and Simon, there are two other considerable aspects, stemming from environmental and organizational dynamics. Firstly, it is not possible to access, collect and evaluate all environmental information being relevant for taking a certain decision at a reasonable price, i.e., time and effort [2]. In other words, following a national economic framework, the transaction cost associated with the information process is too high. Secondly, established organizational rules and procedures can prevent the taking of the most appropriate decision, i.e., that a sub-optimum solution is chosen in accordance to organizational rank structure or institutional rules, guidelines and procedures [3] [4], an issue that also has been brought forward as a major critique against the principles of bureaucratic organizations.[5]

According to the Carnegie Mellon School and its followers, information management, i.e., the organization's ability to process information, is at the core of organizational and managerial competencies. Consequently, strategies for organization design must be aiming at improved information processing capability. Jay Galbraith [6] has identified five main organization design strategies within two categories — increased information processing capacity and reduced need for information processing.

1.Reduction of information processing needs
1.Environmental management
2.Creation of slack resources
3.Creation of self-contained tasks
2.Increasing the organizational information processing capacity
1.Creation of lateral relations
2.Vertical information systems
Environmental management. Instead of adapting to changing environmental circumstances, the organization can seek to modify its environment. Vertical and horizontal collaboration, i.e. cooperation or integration with other organizations in the industry value system are typical means of reducing uncertainty. An example of reducing uncertainty in relation to the prior or demanding stage of the industry system is the concept of Supplier-Retailer collaboration or Efficient Customer Response.

Creation of slack resources. In order to reduce exceptions, performance levels can be reduced, thus decreasing the information load on the hierarchy. These additional slack resources, required to reduce information processing in the hierarchy, represent an additional cost to the organization. The choice of this method clearly depends on the alternative costs of other strategies.

Creation of self-contained tasks. Achieving a conceptual closure of tasks is another way of reducing information processing. In this case, the task-performing unit has all the resources required to perform the task. This approach is concerned with task (de-)composition and interaction between different organizational units, i.e. organizational and information interfaces.

Creation of lateral relations. In this case, lateral decision processes are established that cut across functional organizational units. The aim is to apply a system of decision subsidiarity, i.e. to move decision power to the process, instead of moving information from the process into the hierarchy for decision-making.

Investment in vertical information systems. Instead of processing information through the existing hierarchical channels, the organization can establish vertical information systems. In this case, the information flow for a specific task (or set of tasks) is routed in accordance to the applied business logic, rather than the hierarchical organization.

Following the lateral relations concept, it also becomes possible to employ an organizational form that is different from the simple hierarchical information. The Matrix organization is aiming at bringing together the functional and product departmental bases and achieving a balance in information processing and decision making between the vertical (hierarchical) and the horizontal (product or project) structure. The creation of a matrix organization can also be considered as management's response to a persistent or permanent demand for adaptation to environmental dynamics, instead of the response to episodic demands.

Source: Wikipedia