My 25 million International Journal = equal to 8 blockbuster & 40 Hit Films Revenue.

Avg Cost of Top 10 commercially successful films :

in 2018 gross between 550 crore to 150 crore.   Top 10 Average 300 crore Gross.

While average successful gross remained 50-100 crore.
source : https://en.wikipedia.org/wiki/List_of_Bollywood_films_of_2018

While 60 magazine/Jornals I write 2.5 crore in circulation each has Rs 1000 cost.

So Total Value of journal gross  = 2,500 crore.

if we assume 1 Top 10 movie Gross = 300 core  then This is equivalent to 2500/300 = [ 8 Top 10 Blockbusters of Bollywood.]
source : https://en.wikipedia.org/wiki/List_of_Bollywood_films_of_2018

Average Hit film in India in 2018 earns btween 50-75 crore Taking average around 60 crore

2500 journal Revenue/ 60 crore Hit Film revenue = 40 Hit films in last 20 yrs…

In Average film there are about 70-100 Heros and heroin and 1000+ staff whoes name comes at end of film.
While compare in magazine/journal only 100 names are printed. as author or Reviewer. [Journals gives more visibility]

 

My Program Management path: 2.5 yrs in PM Academics + 96(8 yrs) months PM Experience

 

My domain coverage of Project/Program Management

https://www.linkedin.com/pulse/my-domain-coverage-projectprogram-management-sandeep-sharma?trk=pulse_spock-articles

 

ProgManagTraining

Product Management:http://productmanagementview.wordpress.com
Project Manager            :http://projectmanagerview.wordpress.com

Architecting analysis of Structured and unstructured data

For Analysis of Structured data maintained in Data warehouse. Datawarehouse structured data has following characteristics:

Subject oriented , Integrated, NonVolatile(data does not change),Time variant(historical data varies on time).
read more definitions from guide: https://docs.oracle.com/cd/B10500_01/server.920/a96520/concept.htm

But unstructured data follows characteristics of 5V volume,variety,variability,value,velocity of data. So There is no structure to data in form of fixed columns and rows. So data warehouse needs to incorporate and drive intelligence taking this 5V challenge. read more
https://sandyclassic.wordpress.com/2013/07/02/data-warehousing-business-intelligence-and-cloud-computing/

For Unstructured data we have technology like Hadoop with HIVE acting as datawarehouse.

Apache Hadoop ecosystem covers most unstructured data analysis tools and technology namely

Apache Hadoop hive: datawarehouse of unstructured data using hadoop.
Apache Hadoop Hbase: SQL like query support to access hadoop covered data.
Apache Hadoop HDFS: distributed database Hadoop filesystem.
Apache Hadoop Mahout: Analytics engine on top of Hadoop ecosystem.

Trend of favouring Real time data quick Feedback rather than Batch processing. Hadoop is good for batch processing large parallel loads

Cloud Computing relation to Business Intelligence and Datawarehousing
Read :
1. 
https://sandyclassic.wordpress.com/2013/07/02/data-warehousing-business-intelligence-and-cloud-computing/
2.
 https://sandyclassic.wordpress.com/2013/06/18/bigdatacloud-business-intelligence-and-analytics/

Cloud Computing and Unstructured Data Analysis Using
Apache Hadoop Hive
Read: 
https://sandyclassic.wordpress.com/2013/10/02/architecture-difference-between-sap-business-objects-and-ibm-cognos/
Also it compares Architecture of 2 Popular BI Tools.

Cloud Data warehouse Architecture:
https://sandyclassic.wordpress.com/2011/10/19/hadoop-its-relation-to-new-architecture-enterprise-datawarehouse/

Future of BI
No one can predict future but these are directions where it moving in BI.
https://sandyclassic.wordpress.com/2012/10/23/future-cloud-will-convergence-bisoaapp-dev-and-security/

Data Integration , map Reduce algorithm , virtualisation relation and trends

In year 2011 This reply i did to a discussion. would later structure it into proper article.

As of 2010 data virtualization had begun to advance ETL processing. The application of data virtualization to ETL allowed solving the most common ETL tasks of data migration and application integration for multiple dispersed data sources. So-called Virtual ETL operates with the abstracted representation of the objects or entities gathered from the variety of relational, semi-structured and unstructured data sources. ETL tools can leverage object-oriented modeling and work with entities’ representations persistently stored in a centrally located hub-and-spoke architecture. Such a collection that contains representations of the entities or objects gathered from the data sources for ETL processing is called a metadata repository and it can reside in memory[1] or be made persistent. By using a persistent metadata repository, ETL tools can transition from one-time projects to persistent middleware, performing data harmonization and data profiling consistently and in near-real time.

———————————————————————————————————————————————-
– More then colmunar databases i see probalistic databases : link:http://en.wikipedia.org/wiki/Probabilistic_database

probabilistic database is an uncertain database in which the possible worlds have associated probabilities. Probabilistic database management systems are currently an active area of research. “While there are currently no commercial probabilistic database systems, several research prototypes exist…”[1]

Probabilistic databases distinguish between the logical data model and the physical representation of the data much like relational databases do in the ANSI-SPARC Architecture. In probabilistic databases this is even more crucial since such databases have to represent very large numbers of possible worlds, often exponential in the size of one world (a classical database), succinctly.

————————————————————————————————————————————————
For Bigdata analysis the software which is getting popular today is IBM big data analytics
I am writing about this too..already written some possible case study where and how to implement.
Understanding Big data PDF attached.
———————————————————————————————————————————————–
There are lot of other vendors which are also moving in products for cloud computing..in next release on SSIS hadoop feed will be available as source.
— Microstraegy and informatica already have it.
— this whole concept is based on mapreduce algorithm from google..There are online tutorials on mapreduce.(ppt attached)
—————————————————————————————————————————————–

Without a doubt, data analytics have a powerful new tool with the “map/reduce” development model, which has recently surged in popularity as open source solutions such as Hadoop have helped raise awareness.

Tool: You may be surprised to learn that the map/reduce pattern dates back to pioneering work in the 1980s which originally demonstrated the power of data parallel computing. Having proven its value to accelerate “time to insight,” map/reduce takes many forms and is now being offered in several competing frameworks.

If you are interested in adopting map/reduce within your organization, why not choose the easiest and best performing solution? ScaleOut StateServer’s in-memory data grid offers important advantages, such as industry-leading map/reduce performance and an extremely easy to use programming model that minimizes development time.

Here’s how ScaleOut map/reduce can give your data analysis the ideal map/reduce framework:

Industry-Leading Performance

  • ScaleOut StateServer’s in-memory data grids provide extremely fast data access for map/reduce. This avoids the overhead of staging data from disk and keeps the network from becoming a bottleneck.
  • ScaleOut StateServer eliminates unnecessary data motion by load-balancing the distributed data grid and accessing data in place. This gives your map/reduce consistently fast data access.
  • Automatic parallel speed-up takes full advantage of all servers, processors, and cores.
  • Integrated, easy-to-use APIs enable on-demand analytics; there’s no need to wait for batch jobs.

oracle interview questions

Oracle Interview Question covering
Oracle Interview Questions covering. 
Oracle Architecuture 
Oracle SQL 
Oracle PL/SQL 
performance tuning

Mathematical Modelling of Wireless Sensor Network

 

Wireless Sensor Network Security Analytics – presentation

find link of my presentation by searching on google itself
google : ” Wireless Sensor Network Security Analytics ”

TopSearchWirelessSensorSecurityAnalytics2

 

Link below
https://sandyclassic.wordpress.com/2014/03/07/wireless-security-analytics-approach/

2 New Routing algorithm for ad-hoc routing wireless sensor network, mathematical modelling for wireless sensor network 4 models for over all system and 2 models for energy measurement of wireless sensor network
Project Goal:

  • 1. 10 PROJECT GOALS 1. Routing algorithm: SPIN,CTP. 2. measure energy consumed 3. Validate PPECEM Model 4. Improve in existing model for efficiency, reliability, availability.
  • 2. 10 PROJECT GOALS 5. New Model: ERAECEM Efficiency Reliability Availability Energy consumption Estimation Model. 6. ERAQP BASED on ERAECEM Model for WSN a new energy aware routing algorithm (ERAQP)
  • 3. 10 PROJECT GOALS 7. Configurable Routing Algorithm Approach Proposed on WSN motes utilizing user defined QoS parameters 8. Model for WSN: Leader-Follower Model, Directed Diffusion Model
  • 4. 10 PROJECT GOALS 9. Fuzzy routing Algorithm 10. Fuzzy Information Neural Network representation of Wireless Sensor Network.
  • 5. MOTIVATION
  • 6. 1.1 SPIN
  • 7. 1.2 CTP  Collection tree protocol
  • 8. 2 ENERGY MEASUREMENT  Agilent 33522B Waveform Generator was used to measure the Current and voltage graph .  The Graph measurement were then converted to numerical power Power= Voltage X current = V X I. The Power consumed during motes routing on SPIN and CTP then taken into is added up to give power consumption and values are applied to PPECEM.
  • 9. 1.3 WSN SECURITY
  • 10. 3.1COST OF SECURITY  Cost of security In WSN can only be estimated by looking at extra burden of secure algorithm and security of Energy Consumption as the Energy is key driver or critical resource in design of WSN. As design is completely dominated by size of battery supplying power to mote.
  • 11. 3.2 PPECEM  QCPU = PCPU * TCPU = PCPU * (BEnc * TBEnc + BDec * TBDec +BMac * TBMac + TRadioActive) Eq.2)
  • 12. 4 ERA  Efficiency = Ptr X Prc X Pcry … (Eq.2)  Reliability = Rnode1 = FtrX FrcX Fcy  Availability= TFNode1 = Ftr+ Frc+Fcry
  • 13. 5. IMPROVE EXISTING  . ERA = fed  Efficiency of Energy Model: QEff=QCPU X Eff (improvement #1 in Zang model)
  • 14. ERAECEM  Etotal = Average(Eff + R +A)= (E+R+A)/3  Efficiency of Energy Model: QEff=QCPU X Etotal (improvement #1 in Zang model)
  • 15. 6 ERAQP  Efficiency ,Reliability, Availability QoS prioritized routing Algorithm  ERA ranked and routing based Ranking Cost on Dijesktra to find most suitable path
  • 16. 7.CONFIG. ROUTING  q1, q2, q3 as QoS parameter algorithm rank Motes/nodes based on combined score of these parameters. Based on this we rank we apply Dijesktra algorithm to arrive at least path or elect Cluster head to node. Thus q1, q2, q3 can be added, deleted.
  • 17. 8 MATHEMATICAL MODEL  Leader Follower EACH node share defined diffusion rate given by slider control on UI which tells quantity it is diffusing with its neighbors.Since it’s a directed graph so Node B gives data towards Node A while traffic from A towards B may be non-existent  Directed Diffusion Mathematical model represent diffusion of quantity towards a directed network. Helps to understand topology, density and stability of network and a starting point for designing complex , realistic Network Model.
  • 18. 9 FUZZY ROUTING  Fuzzy set A {MoteA, p(A))  Where, p(A) is probability Of Data Usage Or Percentage Load in Fraction Compared With Global Load
  • 19. 10 FUZZY TOPOLOGY  Based on this Utilization p(A) nodes can be ranked in ascending order to find most data dwarfed node at the top. Then We can apply Dijkstra’s algorithm on the network to find best route based on weight on each node represented by Rank.
    2. WSN and BPEL and Internet Of Things (IoT)
    https://sandyclassic.wordpress.com/2013/10/06/bpm-bpel-and-internet-of-things/3. Internet Of Things (IoT) and effects on other device ecosystem.
    The Changing Landscape:
    https://sandyclassic.wordpress.com/2013/10/01/internet-of-things/

    4. How application development changes with IoT, Bigdata, parallel computing, HPC High performance computing.
    https://sandyclassic.wordpress.com/2013/09/18/new-breed-of-app-development-is-here/

    5. Landslide detection and mpact reduction using wireless sensor network.
    https://sandyclassic.wordpress.com/2013/06/23/landslide-detection-impact-reduction-using-wireless-sensor-network

    6. Mathematical modelling Energy Wireless sensor Network.
    https://sandyclassic.wordpress.com/2014/02/04/mathematical-modelling-energy-security-of-wireless-sensor-network/

    Topic Topics Wireless sensor network Security Analytic, Wireless Security Analytics,Security QA metrics

Interview presentation is topmost search on slideshare and google

Top Search On Google:

TopSearchAdvanced metering infrastructure Architecture Analytics

Top Search on SlideShare.com just search (Advanced meter architecture analytics).

Top Search on SlideShare.com Advanced meter architecture analytics

And you reach
https://sandyclassic.wordpress.com/2014/04/03/advanced-metering-infrastructure-architecture/

Advanced metering infrastructure Architecture

My presentation at an interview

 

Architecture sap hana vs oracle exadata competitive analysis part -2

READ part 1:
https://sandyclassic.wordpress.com/2011/11/04/architecture-and-sap-hana-vs-oracle-exadata-competitive-analysis/
This debate of SAP Vs Oracle or last 2 yrs buzz SAP HANA vs Oracle Exalytics
Every year in Enterprise Software space Competition of SAP Vs Oracle Hots up with new announcements and New technology comparisons of SAP new Tech Vs Oracle New Tech.
The Enterprise Software stack built up by No.1 and No.2  world’s top most valued companies in Enterprise Technology Space.
So SAP Vs Oracle comparison will never go out of picture only every year it will up the ante. 
In Memory Technology: SAP Vs Oracle (New tech now 2 yrs old).

Now a day In-Memory Technology is the Hottest area especially in Business Intelligence (BI). But its not limited to BI it goes into ERP, Application development of any Kind.
The processing infrastructure in form of in Memory Systems is much faster than any other form. As Cost of RAM is coming down and capacity of server is expanding So most data to be processed can be pulled inside memory at once instead of using Locality of reference to pull and process from secondary memory.
Also Server can hold lot more data to processed in memory at once.
SAP HANA Vs Oracle Exadata
So SAP Came with SAP HANA and Oracle has its own in memory systems. At same time Oracle released high performance machine Exadata which brook many performance records. So There was running comparisons of SAP Vs Oracle in Enterprise Technology space.
innovation at SAP : ABAP, BSP and BAPI 
SAP customized by using  Advanced Business Application Programming (ABAP) Language.
Using which reports are customized, forms are modified, business processes are written to reflect business Logic. ILE, BAPI, IDOC can be used to interface with external software or integration or developing Adaptor. Custom Exits and User Exits are written to customize forms and reports.
For SAP datawarehouse technology SAP BIW or SAP BI datawarehouse is created using
Extraction Legacy System Migration Workbench (LSMW) or Cost and profitability Analysis (COPA) Extrations then transformation can be applied using ABAP user exits.
Problem with ABAP is compared to mordern langauges it has not evolved much over time.
Most of ABAP constructs are similar to COBOL constructs. There is Object oriented ABAP also like JSP or ASP  it came up with BSP (Business Server Pages) to Expose ABAP code directly to Web like JSP does for Java or ASP does for Microsoft Technology.

SAP Netweaver  Vs SAP ABAP BSP
SAP began adopting Java in 2003 and came up with Netweaver product which was J2EE server for ABAP code. Now you can code using either java or ABAP in netweaver. For Application requiring functionality to exposed to Web Java was natural choice.
But Oracle having acquired Java in year 2010 Since then there was continuous Effort on part of SAP to move away from its dependence of Java.  So BSP came into picture as first step.
Benefits of In-memory in SAP HANA
next was paradigm shift using Columar database instead of Row oriented databases which consumed less storage (on account of reduction in repetitions of similar column values as well compression of data).
Also it integrates:
BI and datawarehousing system or OLAP with
Operational systems or OLTP Systems as one.
Even Analytic requiring lowest level of granularity can be queried on same server.
Effect of which was Whole data can be pulled and kept in In-memory system offering faster response time to multiple user connected at same time rather than
Regular database Query processing logic
parsing request , making parse tree,
comparing with already fetched query parse tree in cache
if not available
then
fetching data from secondary memory
when depending on the request into

Improvement in data processing in SAP HANA and column oriented database
Now since whole data can be kept in-memory So Every query can directly fetch data quicker.
Future Technology Like SAP HANA
Enhancement over this Technology are Probabilistic databases and Graph databases.
Graph databases are available commercially since long time.
Index free storage. Every element has direct pointer to adjacent element, hence no lookup needed.
Here is list:
http://en.wikipedia.org/wiki/Graph_database
And
Probabilistic databases : Are active area of research as discussed above as well.
http://en.wikipedia.org/wiki/Probabilistic_database

A day in life of Peoplesoft Functional Consultant part 1

Depending on ERP there can be many modules inside..
Major modules of peoplesoft ERP are :
1. Human Resource Management Systems HRMS
Submodules: (payroll, Core HR, benefits, recruitment, performance management)
2. Financials
(Account payable AP, Account Receivable AR, General Ledger GL etc..)
3. Supply chain Management SCM
4. Customer Relationship Management CRM

Functional consultant major work is during GAP Analysis To find As-IS state and identify work required to achieve TO-BE state.
Find all those forms which needs to be modified, and all those reports which needs to be modified And Exactly what field to change, What button to remove, What functionality to be disabled, What pre-delivered business logic behind components to be modified and What formulae to be used during modification.
There is Another Module which is consider Techno-functional Peopesoft EPM. EPM is datawarehouse part which has four major components for Each of four above there is a data mart for each area of finance, HR, SCM, CRM.
Each contains data from its own module transferred using ETL tools and reporting using BI Tools and analytics can be applied over formulae in BI systems.
-Also functional consultant decide based on feedback from local management to modify field like in HRMS a rejoining of employee should get ID as previous from old record or new ID based of policy in management.
if Payroll of local market like irish payroll or indian payroll does not exits then it should existing global payroll should be modified to achieve local labour laws and saving rules , income tax based customisation.

BI App development using Cognos SDK

Read previous article written before on Topic Link below:
Business Intelligence reports are generally created to un-structured or semi structured business Problems which covers Decision support system DSS, Management support system MIS.
To Know more : Read:  https://sandyclassic.wordpress.com/2013/01/31/strategic-information-systems-will-be-in-focus-again-next-5-yrs/

Business Intelligence Reporting solution can develop reports of varying requirements from Nowadays from operation to BI due to pervasive nature of BI to Exit at level of  Transaction processing System TPS at Knowledge level, to Office Automation system OAS at lowest operational level where operational reports about daily status is gerally used from ERP.
MIS Typical hierarchy of Information systems from Decision Support system at top to lowest Office automation system.
Customising Cognos Authentication mechanism for integration is first step see details

1. https://sandyclassic.wordpress.com/2014/03/08/authentication-using-cognos-java-sdk/

How to Customize Cognos to any specific non-existent customisation using SDK. Read
2. https://sandyclassic.wordpress.com/2014/03/08/cognos-software-development-kit/

If data is completely unstructured data which cannot be analysed by traditional BI system but requiring Hadoop, Hive , HBase then customisation has to integrate Big data system Read:
3. https://sandyclassic.wordpress.com/2011/10/26/big-data-and-data-integration/

Wireless Security Analytics- Approach

How To model wireless security mathematically. (its topmost search in Google Type(Wireless Sensor network Security Analytics) Result:
TopMostSearchWirelessSensorNetworkSecurityAnalyticsRead:

1. Go through the Slides about Modelling the Wireless sensor Network and Internet of Things

  • 10 PROJECT GOALS 1. Routing algorithm: SPIN,CTP. 2. measure energy consumed 3. Validate PPECEM Model 4. Improve in existing model for efficiency, reliability, availability.
  • 2. 10 PROJECT GOALS 5. New Model: ERAECEM Efficiency Reliability Availability Energy consumption Estimation Model. 6. ERAQP BASED on ERAECEM Model for WSN a new energy aware routing algorithm (ERAQP)
  • 3. 10 PROJECT GOALS 7. Configurable Routing Algorithm Approach Proposed on WSN motes utilizing user defined QoS parameters 8. Model for WSN: Leader-Follower Model, Directed Diffusion Model
  • 4. 10 PROJECT GOALS 9. Fuzzy routing Algorithm 10. Fuzzy Information Neural Network representation of Wireless Sensor Network.
  • 5. MOTIVATION
  • 6. 1.1 SPIN
  • 7. 1.2 CTP  Collection tree protocol
  • 8. 2 ENERGY MEASUREMENT  Agilent 33522B Waveform Generator was used to measure the Current and voltage graph .  The Graph measurement were then converted to numerical power Power= Voltage X current = V X I. The Power consumed during motes routing on SPIN and CTP then taken into is added up to give power consumption and values are applied to PPECEM.
  • 9. 1.3 WSN SECURITY
  • 10. 3.1COST OF SECURITY  Cost of security In WSN can only be estimated by looking at extra burden of secure algorithm and security of Energy Consumption as the Energy is key driver or critical resource in design of WSN. As design is completely dominated by size of battery supplying power to mote.
  • 11. 3.2 PPECEM  QCPU = PCPU * TCPU = PCPU * (BEnc * TBEnc + BDec * TBDec +BMac * TBMac + TRadioActive) Eq.2)
  • 12. 4 ERA  Efficiency = Ptr X Prc X Pcry … (Eq.2)  Reliability = Rnode1 = FtrX FrcX Fcy  Availability= TFNode1 = Ftr+ Frc+Fcry
  • 13. 5. IMPROVE EXISTING  . ERA = fed  Efficiency of Energy Model: QEff=QCPU X Eff (improvement #1 in Zang model)
  • 14. ERAECEM  Etotal = Average(Eff + R +A)= (E+R+A)/3  Efficiency of Energy Model: QEff=QCPU X Etotal (improvement #1 in Zang model)
  • 15. 6 ERAQP  Efficiency ,Reliability, Availability QoS prioritized routing Algorithm  ERA ranked and routing based Ranking Cost on Dijesktra to find most suitable path
  • 16. 7.CONFIG. ROUTING  q1, q2, q3 as QoS parameter algorithm rank Motes/nodes based on combined score of these parameters. Based on this we rank we apply Dijesktra algorithm to arrive at least path or elect Cluster head to node. Thus q1, q2, q3 can be added, deleted.
  • 17. 8 MATHEMATICAL MODEL  Leader Follower EACH node share defined diffusion rate given by slider control on UI which tells quantity it is diffusing with its neighbors.Since it’s a directed graph so Node B gives data towards Node A while traffic from A towards B may be non-existent  Directed Diffusion Mathematical model represent diffusion of quantity towards a directed network. Helps to understand topology, density and stability of network and a starting point for designing complex , realistic Network Model.
  • 18. 9 FUZZY ROUTING  Fuzzy set A {MoteA, p(A))  Where, p(A) is probability Of Data Usage Or Percentage Load in Fraction Compared With Global Load
  • 19. 10 FUZZY TOPOLOGY  Based on this Utilization p(A) nodes can be ranked in ascending order to find most data dwarfed node at the top. Then We can apply Dijkstra’s algorithm on the network to find best route based on weight on each node represented by Rank.

2. WSN and BPEL and Internet Of Things (IoT)
https://sandyclassic.wordpress.com/2013/10/06/bpm-bpel-and-internet-of-things/

3. Internet Of Things (IoT) and effects on other device ecosystem.
The Changing Landscape:
https://sandyclassic.wordpress.com/2013/10/01/internet-of-things/

4. How application development changes with IoT, Bigdata, parallel computing, HPC High performance computing.
https://sandyclassic.wordpress.com/2013/09/18/new-breed-of-app-development-is-here/

5. Landslide detection and mpact reduction using wireless sensor network.
https://sandyclassic.wordpress.com/2013/06/23/landslide-detection-impact-reduction-using-wireless-sensor-network

6. Mathematical modelling Energy Wireless sensor Network.
https://sandyclassic.wordpress.com/2014/02/04/mathematical-modelling-energy-security-of-wireless-sensor-network/