Archive for January, 2013

ద్వైతము సుఖమా?

January 31, 2013

పల్లవి:  ద్వైతము సుఖమా? అద్వైతము సుఖమా?

అనుపల్లవి: చైతన్యమా! విను సర్వ సాక్షీ!
విస్తారముగాను తెల్పుము నాతో (ద్వైతము)

చరణం: గగన పవన తపన భువనాద్యవనిలో
నగధరాజ శివేంద్రాది సురలలో
భగవద్భక్త వరాగ్రేసరులలో
బాగ రమించే త్యాగరాజార్చిత (ద్వైతము)

శ్రీ త్యాగరాజ స్వామి (వారి ఆరాధన సందర్భంగా పుష్య మాస బహుళ పక్ష పంచమీ )
 
“ఎందరో మహానుభావులు అందరికీ వందనాలు”  అన్న త్యాగరాజ స్వామి వారే ఈ జటిలమైన ప్రశ్న ని శ్రీ రాముల వారినే అడిగారు.

For English: http://sahityam.net/wiki/Dvaitamu_sukhama

సర్వం బ్రహ్మమయం

January 30, 2013

sarvaM brahmamayam rE rE sarvam brahmamayam (pallavi)

1. kim vacanIyam kimavacanIyam kim racanIyam kimaracanIyam – sarvam
2. kim paThanIyam kimapaThanIyaM kim bhajanIyam kimabhajanIyaM – sarvam
3. kim bOdhavyam kimabOdhavyam kim bhOktavyam kimabhOktavyam – sarvam
4. sarvatraH sadA hamsadhyAnam kartavyam bhO mukti nidAnam – sarvam

— SrI sadASiva brahmEndra kIrtana 
సర్వం బ్రహ్మమయం రే రే సర్వం బ్రహ్మమయం (పల్లవి)

1. కిం వచనీయం కిమవచనీయం కిం రచనీయం కిమరచనీయం – సర్వం
2. కిం పఠనీయం కిమపఠనీయం కిం భజనీయం కిమభజనీయం – సర్వం
3. కిం బోధవ్యం కిమబోధవ్యం కిం భోక్తవ్యం కిమభోక్తవ్యం – సర్వం
4. సర్వత్రః సదా హంసధ్యానం కర్తవ్యం భో ముక్తి నిదానం – సర్వం
— శ్రీ సదాశివ బ్రహ్మేంద్ర కీర్తన 

Now my own “English” words!!

Everything ever filled with Brahman! Everything ever filled with Brahman!!

what to talk? what not to talk? what to write? what not to write?
what to study? what not to study? what to worship? what not to worship?
what to understand? what not to understand? what to consume/enjoy? what not to consume/enjoy?
Everywhere always meditate Hamsa that is the duty and the treatment for release/realization

వృద్ధులు – పూజనీయత

January 27, 2013

జ్ఞానవృద్ధో ద్విజాతీనాం క్షత్రియాణాం బలాధికః
వైశ్యానాం ధాన్యధనవాన్ శూద్రాణామేవ జన్మతః

మహాభారతం, సభాపర్వం 38 అధ్యాయం 17 శ్లోకం, భీష్ముడు శిశుపాలుడితో

jnAnavRddhO dvijAtInAM kshatriyANAM balAdhikaH
vaiSyAnAM dhAnyadhanavAn SUdrANAmEva janmataH

mahAbhAratam, sabhAparvam 38 adhyaayam 17 SlOkam, bhIshma to SiSupAla
In the brahmaNas one who has grown in jnAna (knowledge), in the kshatriyas one who has grown powerful, in the vaiSyas one who has grown in wealth and in the Sudras one who has grown merely by age are eligible to be worshipped.

–from SrI pullela SrIrAmaCandruDu gAri mahAbharata sAra sangrahamu

–On the occassion of pushya maasa, pUrnimA, pushyamI nakshatram – “thaipUsam”

Machine Learning Algorithms – Classification, Clustering and Regression

January 25, 2013

For a Data Scientist among the other skill sets, good fundamentals on “data mining” or “machine learning” is the icing over the cake. These algorithms are also used in predictive analytics. It is immaterial if the data is “big data” or not!
We will end up having several variables either containing numeric or nominal attributes describing an entity. The numeric variables could be continuous or discreet like ordinals.  Nominal variables are of the form of known list of values having binary (two i.e., yes or no; male or female etc.,) or more possible values.
Given that a data set consisting of a millions of records, each record containing some 100s of variables an analyst’s job is to derive some insights to solve the known or unknown business problems! This is where the application of machine learning algorithms comes into play.
Broadly Machine Learning can be put into two groups.
1.       Predicting a target variable for a given instance of data record. We will have a set of records with the known values for target variable by which we can develop a model and train the model, test and put that into production – This is called supervised learning
a.       If the target variable is nominal then these algorithms are called classification.
b.      If the target variable is a continuous numeric variable then we need to apply regression
2.       There is no target variable; we need to group the data records into distinct groups based on multiple variables within the dataset – This is called unsupervised learning. Clustering and Association Analysis algorithms are used to achieve this. 
Formulating the problem, preparing the data, visualizing the data, training the model, testing the model and interpreting the results to generate insights and them implementing the derived knowledge to the business operations require multi disciplinary skills in business domain, operations management and technology.
A real business analytic solution consists of using multiple techniques involving machine learning to achieve Customer Segmentation, Cross-selling, Customer behavior analysis, Customer retention, Marketing Analytics and campaign management, fraud detection, optimization of profits etc.,
A recent quick read through the book Machine Learning in Action by Peter Harrington prompted me to write this tech capsule on this Friday… 

కూర్మ చింతనం

January 24, 2013

कूर्मः चिन्तयते पुत्रान् यत्र वा तत्र वा गतान्
चिन्तया वर्थयेत्पुत्रान् यथा कुशलिनः तथा
तव पुत्रास्तु जीवन्ति त्वम् त्राता भरतर्षभ​!

తాబేలు (కూర్మము) తన పిల్లలు ఎక్కడ ఉన్నా వాటిని గూర్చి తలుస్తూ (చింతన చేస్తూ) ఉంటుందట. అవి పెరుగుతాయట. ఆ విధంగా పుత్రులను పెంచావు. నువ్వే నా పుత్రులను బ్రతికించావు.

— కుంతి విదురుని తో (మహాభరతం, ఆది పర్వం 206.12)

“A tortoise just thinks about its children where ever they are. Just by the parent tortoise thinking about them the children grow up and be protected. In the same way you have protected my children.”

— These are the words of Kunti to Vidura in Mahabharata, Adi parvam 206 chapter, 12th Sloka

Just like a parent tortoise, a Guru can take care of his disciples just by thinking about them. My Guru protects me just in this way which makes me remember this verse.

on Intutive Knowledge by Swami Vivekananda

January 12, 2013

Today, 12th January 2013 is the 150th birth anniversary of Swami Vivekananda. He has influenced several people and I am one among them. (See my past post on his influence on me- http://nonenglishstuff.blogspot.in/2011/11/blog-post_21.html )

Would like to put up a simple thought of Swami from “Swami Vivekananda an Intuitive Scientist” – http://www.chennaimath.org/istore/product/swami-vivekananda-an-intuitive-scientist/

om tat sat

A journey through Grid and Virtualization leading to Cloud computing

January 11, 2013

In computing these are two trends I have seen crisscrossing throughout my career.
1.       Making a computing node look like multiple nodes using virtualization.
2.       Making multiple computing nodes work as a single whole called a cluster or grid
So, there was an era where the computing was really a “Big-Iron” and the computers are mainframe size and provided a lot of capacity of computing. One computer handled multiple users and multiple operations at the same time.  The Virtual Machine is included in operating system of IBM and the mainframes as well as from DEC VAX mainframes had similar concepts. Of late, we see the trend even in desktops with hypervisors like vmware etc., coming out.
With the advent of mid-range servers with limited capacity, there is a need to put them together to get the higher computing power to deal with the demand.  The first commercial cluster developed by DEC ARCnet even though there is always been a fight between IBM and DEC on who invented clusters.  Clustering also provides high availability / fault tolerance along with higher computing capacity.  Oracle was the first database to implement parallel server on ARCnet cluster for VAX operating system.
This trend of cluster computing has achieved supercomputing to break the complex task in to multiple parallel streams and execute them on multiple processors. What is the fundamental challenge in “clustering” – the process coordination and access to the shared resources. This leads to be locally networked and connected with high performance local network.
Another concept of this is grid computing where the administrative domain can connect loosely coupled nodes to perform a task. So, we have more and more cores, processors, nodes in a grid to provide low cost, fault tolerant computing. This is making smaller components put together to look like a giant computing capacity.
Finally, what I see today is “Cloud” which creates a grid of elastic nodes that look to appear like a single (large) computing resource and gives a slice of virtualized capacity to each of multiple tenants of that computing resource.  
Designing solutions in each of these technologies of big-iron, virtualization, clusters & grid and in Cloud world has really been challenging and keeps the job lively…

రస దృష్టి

January 10, 2013

शिवे शृंगारार्द्रा तदितरजने कुत्सनपरा
सरोषा गङ्गायां गिरिशचरिते विस्मयवती ।
हराहिभ्यो भीता सरसिरुह सौभाग्य जननी
सखीषु स्मेरा ते मयि जननि दृष्टिः स करुणा ॥

శివే శృంగారార్ద్రా తదితరజనే కుత్సనపరా
సరోషా గఞ్గాయాం గిరిశచరితే విస్మయవతీ  ।
హరాహిభ్యో భీతా సరసిరుహ సౌభాగ్యజననీ
సఖీషు స్మేరా తే మయి జనని దృష్టిః సకరుణా ॥

SivE SRMgArArdrA taditarajanE kutsanaparA
sarOshA ga~ngAyAm giriSacaritE vismayavatI
harAhibhyO bhiitaa sarasiruha saubhAgyajananI
sakhIshu smErA tE mayi janani dRshTiH sakaruNA

— saundarya laharI (51 slOka) of Adi SankarAcArya

The expression towards Siva is sRingAra, others (who feel they are ‘Men’ in front of you) kutsana i.e., disgust or bhIbhatsa. towards Ganga it is rOsha i.e., raudra. Listening to the narrations of giriSa i.e, Siva the expression is vismaya i.e, adbhuta or wonderment. Thy eyes show the expression of fear i.e, bhIta or bhayAnaka rasa towards the ‘ahi’ the snakes that adore Lord Siva. The red tinge like the red-lotus in the eyes signify the vIra rasa that generates the saubhAgya. Towards thy sakhIs i.e, friends, hasya rasa is expressed. Towards me (the devotee) thy expression is always karUnA i.e. compassion.

Out of nava rasas, eight are covered above and “sAnta rasa” is not. Why? divine mother’s nature is pure sAta which is the basis of all the rest of rasas.

om tat sat

జడ భరత ఆఖ్యానము – Jada Bharata’s Teaching

January 7, 2013

Srimad Bhagavatam is a PurANa. The primary difference between modern history and purANa:

The modern history can only tell Mr X was born on so and so date, he has conquered so and so country, he acted such and such way and he died on so and so date. That’s it.

Where as an ItihAsa (iti + hA + asau means “this is how it happened!”) of a purANa explains why Mr. X was born as Mr.X, What happened during & after Mr.X’s lifespan and what is the conclusion to be derived from the incident of Mr.X’s narration. So, the reader can immediately benefited by learning from Mr. X’s mistakes and start following the righteous path (i.e., dharma)

There is absolutely no compulsion to study purANas and ItihAsAs if one is not interested in knowing true and useful history of mankind.

Narration of Jada Bharata:

Bharata (This country is called as bhArata varsha due to him being the emperor of this land!) was the son of Rishabdeva. He righteously ruled this land for a long time and while he was still healthy and capable, retired to forests giving up all the worldly attachments.

Towards end of his life sitting at the bank of river Gandaki he saw a small baby deer floating in the water. He rescued it and started looking after the deer. He has developed attachment to the deer and in the final stage of his life thinking of the deer he died.

Due to the attachment to the deer while dying, he was born as a deer in the next life. Due to his penance in past life, he remembered his past life of King Bharata and lived the life of deer with devotion to Lord Hari near a temple and in the due course he left the deer’s body.

As the next birth, he was born in a Brahmin family to pious Brahmana posessing sama, dama, tapas and tyaga. But he remembered his life as a deer as well as the King Bharata and started being indifferent to the worldly pleasures. There by he was called as JaDa. (non responsive) He was called as adhama brAhmaNa and jaDa bharata by fellow citizens.

King RahoogaNa going to meet sage kapila needed ONE bearer of his palanquin. He has seen jaDa bharata on the path and employed as the bearer. As jaDa bharata missed the steps and gone out of sync with other bearers thereby causing discomfort to the King RahoogaNa. Having repeatedly scolded by the King, Sage jaDa bharata makes a statement:

“You are talking of path, porter and punishment (to me), but none of these exist for me. If there is load, it is for the one who carries it; path is for the walker. Fat, thin, sickness, hunger, pain, fear, quarrel, desire, young, old, sleep, love, hate, anger, pretty and ugly are all present in a person who is body-conscious, they are not in me. All that you say apply only to the body, not to the Atma. ‘I am king, you are servant,’ such feelings are only in the practical world, not in Adhytma (spiritual world.) If you think you should punish me, you may punish, or beat the body; it will not affect me. I live in my own world, unconcerned about the body. Your punishment will be like grinding powdered stone or like beating a lifeless stone.”

With these words, King RahoogaNa realized that the person carrying his palanquin was a great sage and comes down and falls at the feet of jaDa bharata.

There is a wonderful discussion that follows between the King and jaDa bharata leading to the discourse of ultimate truth.

So, this brief is shared here for the readers to study the bharata akhyAna from Fifth Canto of Srimad Bhagavata PurANa.

om tat sat.

Small & Big Data processing philosophies

January 3, 2013
In this first post of 2013, I would like to cover some fundamental philosophical aspects of “data” & “processing”.
As the buzz around “Big Data” going on high, I have classified the original structured, relational data as “small data” even though some very large databases I have seen having 100+ Terabytes of data with an IO volume of 150+ Terabytes per day.  
Present day data processing predominantly uses Von-Neumann architecture of computing in which “Data” and its “processing” are distinct and separated into “memory” and “processor” connected by a “bus”.  Any data that need to be processed will be moved into processor using the bus and then the required arithmetic or logical operation happens on it producing the “result” of the operation. Then the result will be moved to “memory/storage” for further reference.  Also, the list of operations to be performed (the processing logic or program) is stored in the “memory” as well. One needs to move the next instruction to be carried out into the processor from memory using the bus.
So in essence both the data and the operation that needs performing will be in memory which can’t process data and the facility that can process data is always dependent on the memory in the Von-Neumann architecture.
Traditionally, the “data” has been moved into a place where the processing logic is deployed as the amount of data is small when compared to the amount of processing needed is relatively large involving the complex logic. In the RDBMS engines like Oracle read the blocks of storage into the SGA buffer cache of running database instance for processing. The transactions were modifying small amounts of data at any given time.
Over a period of time “analytical processing” that required to bring huge amounts of data from storage into processing node which created a bottleneck on the network pipe. Add to that there is a large growth in the semi-structured and unstructured data that started flowing which needed a different philosophy towards data processing.
There comes the HDFS and map-reduce framework of Hadoop which took the processing to the data. During the same time comes Oracle Exadata which took the database processing to storage layer with a feature called “query offloading”
In the new paradigm, the snippets of processing logic are being taken to a cluster of connected nodes where the data mapped with a hashing algorithm resides and results of processing then reduced to finally produce result sets. It is now becoming economical to take the processing to data as the amount of data is relatively large and the required processing is fairly simple tasks of matching, aggregating, indexing etc.,
So, we now have choice of taking small amounts of data to complex processing with structured RDBMS engines with shared-everything architecture of traditional model as well as taking processing to data in the shared-nothing big data architectures. It purely depends on the type of “data processing” problem in hand and neither traditional RDBMS technologies will be replaced by new big data architectures, nor could the new big-data problems be solved by traditional RDBMS technologies. They go hand-in-hand complementing each other while adding value to the business when properly implemented.
The non-Von-Neumann architectures still need better attention by the technologists which will probably hold the key to the way human brain processes and deals with the information seamlessly either it is structured or non-structured streams of voice, video etc., with ease. 
Any non-Von-Neumann architecture enthusiasts over here?