Citations & References:"Before dawn, Hanuman, who had transformed into a little monkey, leapt lightly from roof to roof, and like a silvery hallucination before him, he saw Ravana’s palace silhouetted against the sleeping ocean. It was as if a small slice of another, supernal, world had fallen into this one. Shimmering towers and turrets reached for the stars on their way across the sky. Within that great palace, Hanuman sensed an implacable evil, a quenchless thirst to dominate," the Moon began again. "Through a golden gate set with corals and pearls bigger than any he had ever seen, he stole on tiny feet into the antapura, Ravana’s harem. He found the Lord of Lanka was a collector not only of rakshasis. Some wings of the harem were full of sleeping gandharva women, lovely beyond belief; their hair glimmered with the natural starlight that is their elfin heritage, and their gossamer skins seemed to be woven from moonbeams.In other antapuras, slept strong-limbed kinnara women with high cheekbones, whose men are centaurs. In yet other apartments were chambers full of green and serpentine naga women, sinuously exquisite, with jewels embedded in their sleeping heads. It struck Hanuman that all these women were Ravana’s lovers. It did not seem to the vanara they were restrained here as captives; they slept much too languorously. But probably, they fell asleep because they were tired, thinking about the investigation warrant issued by Ravana.The little monkey shook his head at the ways of fate. Here was a sovereign who had delectable mistresses from every race in the three worlds; yet he chose to court death at Rama’s hands. And Hanuman believed that, however impossible it might seem to him just now, death would come ineluctably for Ravana.The Dawn has rolled up his sleeves, and little Hanuman was engrossed in touring the city of Ravana. He could faintly hear a commotion from the Great Hall. He came closer, and at the gate, there was a large banner that read 'People's Forum.' Instantly, Hanuman jumped onto the roof, peeking through its gap. He saw, The Emperor stood at the podium, but didn't listen much of what Ravana said. He noticed it was not Alengka's people, but the harems were there, acting as the people. Ravana was delivering his speech, 'If the ciscumtances not possible, I will promote Indrajit as the Emperor of Lanka. And I myself, will be the vice emperor. At last, let me wish my younger sister a Happy Birthday, Surpanaka.' Then, the audience stood to sing 'Happy Birthday.'Few really know why Ravana was portrayed as the ten-headed, twenty-armed figure as the supreme anti-hero. The great King of Asura, Mahabali, once advised Ravana to shun ten base emotions: anger; pride; arrogance; jealousy; happiness; sadness; fear; selfishness; passion and ambition. Mahabali spoke in length about mind control and mastering the senses., 'Anger is the lowest emotion. It clouds the intellect and can make you do foolish things. You become blind to reason and react only with your body, without thinking. This leads to failure in every sphere. Uproot this evil from your system.The next base emotion is Pride. Arrogance stems from pride and kills clear thinking and vision. Pride makes you underestimate your foes and overestimate yourself. Jealousy is a vile emotion, and mastering it is one of the most challenging tasks a human being has. Jealousy makes you pine for other man’s kingdom, wealth, wife and fame. This emotion has lead to many wars, bloodshed and tears since time immemorial.Happiness and sadness are just two eternal truths like day and night. A man of pure heart and superior intellect, never affected by these emotions. They are not base emotions at all but a reflection of our thoughts, a reaction to our perspective on things we see, hear and do. Equanimity is not only desirable in a warrior, but a must. Without it, you are as good as dead in the battlefield.Fear is not an emotion, it is a disease. It spreads from the leader to his followers and vice–versa. Nothing has killed more men in war than fear. What should a warrior fear? Death? But death is what everyone achieves ultimately. Is it wounds that you fear? What is more important? A pint of your blood or the nectar of victory? Think. Thinking will clear such doubts.Nothing is more condemnable than selfishness. A man who thinks of himself alone is the most unlucky person of all. Why is one born? Is it to get fed and grow fat? Is to procreate and multiply like pigs? Is to defile this good earth with bodily wastes and then die without creating any ripples in the world? What is his life worth if it does not light at least a small light in the darkness that is crushing our people. Abhor this vile emotion of selfishness.Passion is a chain that ties you to the millstone of make-belief. A warrior should focus on victory and victory alone. That should be your only Dharma. Do your duty to your people, parents, wives, sisters, brothers and Gods. Passion makes you weak. Passion has unseen bondages that take you into the abyss of failure at that crucial moment when victory and failure get balanced. Beware of passion.Finally, control your ambition. Ravana, I can see the fiery ambition burning in your eyes. But do not be reckless. Take only what life offers you as your own. Let your life follow its own tide. Aim for things and strive to achieve them, but always keep your feet solidly on the ground. Think, think and think, before you act.The only thing worth preserving is your mind. Your mind absorbs the knowledge you gain from your Gurus, your books and your life, and refines it to great wisdom. It is what you have to develop. Every living minute you have to strive to feed your mind with fresh and positive inputs. This will give clarity to your vision and immense power to your action. You will make fewer mistakes and also learn faster from them.Finally, control your ambition. Ravana, I can see the fiery ambition burning in your eyes. But do not be reckless. Take only what life offers you as your own. Let your life follow its own tide. Aim for things and strive to achieve them, but always keep your feet solidly on the ground. Think, think and think, before you act.The only thing worth preserving is your mind. Your mind absorbs the knowledge you gain from your Gurus, your books and your life, and refines it to great wisdom. It is what you have to develop. Every living minute you have to strive to feed your mind with fresh and positive inputs. This will give clarity to your vision and immense power to your action. You will make fewer mistakes and also learn faster from them.Finally, control your ambition. Ravana, I can see the fiery ambition burning in your eyes. But do not be reckless. Take only what life offers you as your own. Let your life follow its own tide. Aim for things and strive to achieve them, but always keep your feet solidly on the ground. Think, think and think, before you act.The only thing worth preserving is your mind. Your mind absorbs the knowledge you gain from your Gurus, your books and your life, and refines it to great wisdom. It is what you have to develop. Every living minute you have to strive to feed your mind with fresh and positive inputs. This will give clarity to your vision and immense power to your action. You will make fewer mistakes and also learn faster from them.But beware, do not make your intellect a mere decoration. Decorate it before and or after your name. Do not use your reason, seek to justify your own ambitions, or hiding the truth with your left hand.'But, in his response to Mahabali, Ravana justifies and exults in the possession of all these ten facets, as they make him a complete man. That's why Ravana portrays as Dasamukha, or the ten-faced one, while his twenty hands denote prowess and power. Generally in Wayang, the rakshahasas are described as having only one left-hand, while the right-hand is tied. It means that their behavior and actions are always 'left-side' [a metaphor, nothing to do with left-handed], or bad deed. So, it can be said that, each rakshasa is always depicted as bad behavior, their eyes are big, and some are not open at all, 'blinded,' an upward glance symbolized arrogance, mouth wide openned and showing canine symbolized often intimidate people. In short, they are presenting a greedy character and belittle others. They are described as 'blind, lustful, continually mistreat others.'Furthermore, the presenter announced that the next speaker would be Vibeeshana, as a resource person. And having stood on the pulpit, he said, 'Like it or not, data is playing an increasingly important role in all of our lives—and its role is going to get larger. Newspapers now have full sections devoted to data. Companies have teams with the exclusive task of analyzing their data. Investors give start-ups tens of millions of dollars if they can store more data. Even if you never learn how to run a regression or calculate a confidence interval, you are going to encounter a lot of data—in the pages you read, the business meetings you attend, the gossip you hear next to the watercoolers you drink from. Many people are anxious over this development. They are intimidated by data, easily lost and confused in a world of numbers. They think that a quantitative understanding of the world is for a select few left-brained prodigies, not for them. As soon as they encounter numbers, they are ready to turn the page, end the meeting, or change the conversation. And let me tell you this: 'Good data science is less complicated than people think. The best data science, in fact, is surprisingly intuitive. What makes data science intuitive? At its core, data science is about spotting patterns and predicting how one variable will affect another. People do this all the time.Just think, when you were a kid, you noticed that when you cried, your mom gave you attention. That is data science. When you reached adulthood, you noticed that if you complain too much, people want to hang out with you less. That is data science, too. When people hang out with you less, you noticed, you are less happy. When you are less happy, you are less friendly. When you are less friendly, people want to hang out with you even less. Data science. And you are the data scientist.But wait, we are often wrong about how the world works when we rely just on what we hear or personally experience. When relying on our gut, we can also be thrown off by the basic human fascination with the dramatic. We tend to overestimate the prevalence of anything that makes for a memorable story. For example, when asked in a survey, people consistently rank tornadoes as a more common cause of death than asthma. In fact, asthma causes about seventy times more deaths. Deaths by asthma don’t stand out—and don’t make the news. Deaths by tornadoes do. While the methodology of good data science is often intuitive, the results are frequently counterintuitive. Data science takes a natural and intuitive human process—spotting patterns and making sense of them—and injects it with steroids, potentially showing us that the world works in a completely different way from how we thought it did. The goal of a data scientist is to understand the world. Once we find the counterintuitive result, we can use more data science to help us explain why the world is not as it seems.Let us dig into the history. In 431 BCE, Sparta declared war on Athens. Thucydides, in his account of the war, describes how besieged Plataean forces loyal to Athens planned to escape by scaling the wall surrounding Plataea built by Spartan-led Peloponnesian forces. To do this they needed to know how high the wall was so that they could make ladders of suitable length. Much of the Peloponnesian wall had been covered with rough pebbledash, but a section was found where the bricks were still clearly visible and a large number of soldiers were each given the task of counting the layers of these exposed bricks. Working at a distance safe from enemy attack inevitably introduced mistakes, but as Thucydides explains, given that many counts were taken, the result that appeared most often would be correct. This most frequently occurring count, which we would now refer to as the mode, was then used to calculate the height of the wall, the Plataeans knowing the size of the local bricks used, and ladders of the length required to scale the wall were constructed. This enabled a force of several hundred men to escape, and the episode may well be considered the most impressive example of historic data collection and analysis. But the collection, storage, and analysis of data pre-dates even Thucydides by many centuries.Notches have been found on sticks, stones, and bones dating back to as early as the Upper Paleolithic era. These notches are thought to represent data stored as tally marks, though this is still open to academic debate. Perhaps the most famous example is the Ishango Bone, found in the Democratic Republic of Congo in 1950, and which is estimated to be around 20,000 years old. This notched bone has been variously interpreted as a calculator or a calendar, although others prefer to explain the notches as being there just to provide a examples of data usage are by no means confined to Europe and Africa. The Incas and their South American predecessors, keen to record statistics for tax and commercial purposes, used a sophisticated and complex system of coloured knotted strings, called quipu, as a decimal-based accounting system. These knotted strings, made from brightly dyed cotton or camelid wool, date back to the third millennium BCE, and although fewer than a thousand are known to have survived the Spanish invasion and subsequent attempt to eradicate them, they are among the first known examples of a massive data storage system. Computer algorithms are now being developed to try to decode the full meaning of the quipu and enhance our understanding of how they were used.Although we can think of and describe these early systems as using data, the word ‘data’ is actually a plural word of Latin origin, with ‘datum’ being the singular. ‘Datum’ is rarely used today and ‘data’ is used for both singular and plural. The Oxford English Dictionary attributes the first known use of the term to the 17th-century English cleric Henry Hammond in a controversial religious tract published in 1648. In it Hammond used the phrase ‘heap of data’, in a theological sense, to refer to incontrovertible religious truths. But although this publication stands out as representing the first use of the term ‘data’ in English, it does not capture its use in the modern sense of denoting facts and figures about a population of interest. ‘Data’, as we now understand the term, owes its origins to the scientific revolution in the 18th century led by intellectual giants such as Priestley, Newton, and Lavoisier; and, by 1809, following the work of earlier mathematicians, Gauss and Laplace were laying the highly mathematical foundations for modern statistical methodology.On a more practical level, an extensive amount of data was collected on the 1854 cholera outbreak in Broad Street, London, allowing physician John Snow to chart the outbreak. Following John Snow’s work, epidemiologists and social scientists have increasingly found demographic data invaluable for research purposes, and the census now taken in many countries proves a useful source of such information.Before the widespread use of computers, data from the census, scientific experiments, or carefully designed sample surveys and questionnaires was recorded on paper—a process that was time-consuming and expensive. Data collection could only take place once researchers had decided which questions they wanted their experiments or surveys to answer, and the resulting highly structured data, transcribed onto paper in ordered rows and columns, was then amenable to traditional methods of statistical analysis.The terms ‘Internet’ and ‘World Wide Web’ are actually very different. The Internet is a network of networks, consisting of computers, computer networks, local area networks (LANs), satellites, and cellphones and other electronic devices, all linked together and able to send bundles of data to one another, which they do using an IP (Internet protocol) address. The World Wide Web (www, or Web), described by its inventor, T. J. Berners-Lee, as ‘a global information system’, exploited Internet access so that all those with a computer and a connection could communicate with other users through such media as email, instant messaging, social networking, and texting. Subscribers to an ISP (Internet services provider) can connect to the Internet and so access the Web and many other services.By the first half of the 20th century some data was being stored on computers, helping to alleviate some of this labour-intensive work, but it was through the launch of the World Wide Web (or Web) in 1989, and its rapid development, that it became increasingly feasible to generate, collect, store, and analyse data electronically. The problems inevitably generated by the very large volume of data made accessible by the Web then needed to be addressed, and we first look at how we may make distinctions between different types of data. addressed, and we first look at how we may make distinctions between different types of data. The data we derive from the Web can be classified as structured, unstructured, or semi-structured.Structured data, of the kind written by hand and kept in notebooks or in filing cabinets, is now stored electronically on spreadsheets or databases, and consists of spreadsheet-style tables with rows and columns, each row being a record and each column a well-defined field (e.g. name, address, and age). We are contributing to these structured data stores when, for example, we provide the information necessary to order goods online. Carefully structured and tabulated data is relatively easy to manage and is amenable to statistical analysis, indeed until recently statistical analysis methods could be applied only to structured data.In contrast, unstructured data is not so easily categorized and includes photos, videos, tweets, and word-processing documents. Once the use of the World Wide Web became widespread, it transpired that many such potential sources of information remained inaccessible because they lacked the structure needed for existing analytic techniques to be applied. However, by identifying key features, data that appears at first sight to be unstructured may not be completely without structure. Emails, for example, contain structured metadata in the heading as well as the actual unstructured message in the text and so may be classified as semi-structured data. Metadata tags, which are essentially descriptive references, can be used to add some structure to unstructured data. Adding a word tag to an image on a website makes it identifiable and so easier to search for. Semi-structured data is also found on social networking sites, which use hashtags so that messages (which are unstructured data) on a particular topic can be identified. Dealing with unstructured data is challenging: since it cannot be stored in traditional databases or spreadsheets, special tools have had to be developed to extract useful information. The term ‘data explosion’, refers to the increasingly vast amounts of structured, unstructured, and semi-structured data being generated minute by minute.Approximately 80 per cent of the world’s data is unstructured in the form of text, photos, and images, and so it is not amenable to the traditional methods of structured data analysis. ‘Big data’ is now used to refer not just to the total amount of data generated and stored electronically, but also to specific datasets that are large in both size and complexity, with which new algorithmic techniques are required in order to extract useful information from them. These big datasets come from different sources.All of this data, came from the stream of, among them, search engine, healthcare data, real-time data, astronomical data, and so forth. Big data is used extensively in commerce and medicine and has applications in law, sociology, marketing, public health, and all areas of natural science. It is now almost impossible to take part in everyday activities and avoid having some personal data collected electronically. Supermarket check-outs collect data on what we buy; airlines collect information about our travel arrangements when we purchase a ticket; and banks collect our financial data. Data in all its forms has the potential to provide a wealth of useful information if we can develop ways to extract it. New techniques melding traditional statistics and computer science make it increasingly feasible to analyse large sets of data. These techniques and algorithms developed by statisticians and computer scientists search for patterns in data. Determining which patterns are important is key to the success of big data analytics. The changes brought about by the digital age have substantially changed the way data is collected, stored, and analysed. The big data revolution has given us smart cars and home-monitoring.Big data didn’t just happen—it was closely linked to the development of computer technology. In the digital age we are no longer entirely dependent on samples, since we can often collect all the data we need on entire populations. But the size of these increasingly large sets of data cannot alone provide a definition for the term ‘big data’—we must include complexity in any definition. Instead of carefully constructed samples of ‘small data’ we are now dealing with huge amounts of data that has not been collected with any specific questions in mind and is often unstructured. In order to characterize the key features that make data big and move towards a definition of the term, Doug Laney, writing in 2001, proposed using the three ‘v’s: volume, variety, and velocity. ‘Volume’ refers to the amount of electronic data that is now collected and stored, which is growing at an ever-increasing rate. Big data is big, but how big? Generally, we can say the volume criterion is met if the dataset is such that we cannot collect, store, and analyse it using traditional computing and statistical methods. A great variety of data is collected by hospitals, the military, and many commercial enterprises for a number of purposes, ultimately it can all be classified as structured, unstructured, or semi-structured. Velocity also refers to the speed at which data is electronically processed.Why Big Data matters? Big data, big business. In the 1920s, J. Lyons and Co., a British catering firm famous for their ‘Corner House’ cafés, employed a young Cambridge University mathematician, John Simmons, to do statistical work. In 1947, Raymond Thompson and Oliver Standingford, both of whom had been recruited by Simmons, were sent on a fact-finding visit to the USA. It was on this visit that they first became aware of electronic computers and their potential for executing routine calculations. Simmons, impressed by their findings, sought to persuade Lyons to acquire a computer.Collaboration with Maurice Wilkes, who was then engaged in building the Electronic Delay Storage Automatic Computer (EDSAC) at the University of Cambridge, resulted in the Lyons Electronic Office. This computer ran on punched cards and was first used by Lyons in 1951 for basic accounting tasks, such as adding up columns of figures. By 1954, Lyons had formed its own computer business and was building the LEO II, followed by the LEO III. Although the first office computers were being installed as early as the 1950s, given their use of valves (6,000 in the case of the LEO I) and magnetic tape, and their very small amount of RAM, these early machines were unreliable and their applications were limited. The original Lyons Electronic Office became widely referred to as the first business computer, paving the way for modern e-commerce and, after several mergers, finally became part of the newly formed International Computers Limited (ICL) in 1968.The LEO machines and the massive mainframe computers that followed were suitable only for the number-crunching tasks involved in such tasks as accounting and auditing. Workers who had traditionally spent their days tallying columns of figures now spent their time producing punched cards instead, a task no less tedious while requiring the same high degree of accuracy.The eminent economist, John Maynard Keynes, writing during the British economic depression in 1930, speculated on what working life would be like a century later. The industrial revolution had created new city-based jobs in factories and transformed what had been a largely agrarian society. It was thought that labour-intensive work would eventually be performed by machines, leading to unemployment for some and a much-reduced working week for others. Keynes was particularly concerned with how people would use their increased leisure time, freed from the exigencies of gainful employment by technological advances. Perhaps more pressing was the question of financial support leading to the suggestion that a universal basic income would provide a way of coping with the decline in available jobs.Since the use of computers became feasible for commercial enterprises, there has been interest in how they can be used to improve efficiency, cut costs, and generate profits. The development of the transistor and its use in commercially available computers resulted in ever-smaller machines, and in the early 1970s the first personal computers were being introduced. However, it was not until 1981, when International Business Machines (IBM) launched the IBM-PC on the market, with the use of floppy disks for data storage, that the idea really took off for business. The word-processing and spreadsheet capabilities of succeeding generations of PCs were largely mobile smart devices and facilities such as the electronic signature.Although the optimistic aspiration of the early digital age to make an office paperless has yet to be fulfilled, the office environment has been revolutionized by email, word-processing, and electronic spreadsheets. But it was the widespread adoption of the Internet that made e-commerce a practical proposition.Online shopping is perhaps the most familiar example. As customers, we enjoy the convenience of shopping at home and avoiding time-consuming queues. The disadvantages to the customer are few but, depending on the type of transaction, the lack of contact with a store employee may inhibit the use of online purchasing. Increasingly, these problems are being overcome by online customer advice facilities such as ‘instant chat’, online reviews, and star rankings, a huge choice of goods and services together with generous return policies. As well as buying and paying for goods, we can now pay our bills, do our banking, buy airline tickets, and access a host of other services all online.As a closing, let me conclude that the more data you gather from your customers, the more value you can provide to them. And the more you can deliver to the, the higher the profit you can make.Many companies go into big data simply because every big name in their industry is in to it. Unfortunately, they take a big data plunge without realizing why it matters to them. In the end they end up drowning in the sea of information that starts to clog up the data management system they deploy to handle big data. One has to understand why big data matters and how it can make a difference to his company’s operations before one can draw value from it.'Afterwards, Hanuman immediately moved from his hiding place, in order to find data about Sita. Exhausted by now, Hanuman told himself, ‘Rama said that Sita loves flowers, trees and all wild things, deer, squirrels and birds. He said she spoke to them as if she knew each one’s tongue. The stream is cool and pure. Perhaps she will come to touch its water and greet the sun at dawn.’He whispered on to himself like this. He dare not relinquish hope; his very life hung by just that thread. Like many creatures of the jungle, he could see almost as clearly by night as by day. As his eyes grew accustomed to the darkness, Hanuman marvelled at the great garden he had come into. It was at least as lovely as Indra’s Nandana or Kubera’s Chaitra.The scents, which were wafted on the night, reminded him of Gandhamadana, the fragrant mountain, to which Hanuman had once come during Sugriva’s long flight from Vali. Hanuman did not know this, but the scents of Ravana’s asokavana were heavenly because the plants, shrubs and trees that grew here had sprouted from seeds brought down from Nandana and Chaitra themselves.As his eyes saw more clearly, Hanuman peered out sharply from his perch. Ahead of him, glowing through the darkness, was a little temple supported by white pillars all around, its arches overgrown with ivy.Hanuman shinned down his tree and crept towards that temple. He saw that the pathway leading to the domed edifice was paved entirely with slabs of the red stone of the sea. He saw the steps that led up to it were also of dark coral. As he came nearer, he saw the little shrine glowed because its outer walls had been gilded with molten gold.He heard the sound of snoring, and then someone sigh softly. In a flash, Hanuman darted his little head round the arched ingress, and his eyes grew round and his heart gave a lurch. Her yellow silk was soiled, her face was stained with tears, and she sighed from time to time amidst the rakshasis who lay asleep around her. But she shone in that shrine and there was no doubt in the vanara’s mind: she was Rama’s love, this was Sita!"
- Ramesh Menon, The Ramayana: A modern Translation, HarperCollins
- Bibeck Debroy, The Valmiki Ramayana, Penguin Books
- Ir. Sri Mulyono, Wayang dan Karakter Manusia - Nenek Moyang Kurawa dan Pandawa, CV Haji Masagung
- Anand Neelakantan, Asura: Tale of the Vanquished - The Story of Ravana and His People, Platinum Press
- Seth Stephens-Davidowitz, Everybody Lies: Big Data, New Data and What the Internet Can Tell Us about Who We Really are, Dey St.
- Dawn E. Holmes, Big Data - A Very Short Introduction, Oxford University Press
- Vince Reynolds, Big Data for Beginners, Createspace Independent Publishing Platform
[Part 6]
[Part 4]
