We live in the world of Big Data, since we have get used to get access to different information we need through the Internet by using every possible kinds of mobile devices such as smartphone and computer. We could easily feel that whenever we want to get any kinds of data its always there in the cloud and powerful. An old thought comes that if you have a really big computer and you feed it a huge amount of information then you can answer almost any question. But it turns out not that exactly. To defining Big Data, it must cater to five key points at the same time, the volume, variety, velocity, designed, operated and analyzed data resource and distinguish from massive data. However, what makes the Big Data powerful and overwhelming is messiness. Compared with the Small Data, which has high accuracy, less errors and high quality, Big Data allows imprecision. It’s not mean that we already or have to get used to live with errors, but the inaccuracy of Big Data do provide us a bright future, at least for now. Big Data as it is named means a huge volume of data that collected together, which has far more data storage than which Small Data has. In fact, more data points offer far greater value offset their messiness. We give up exactitude for frequency and then in return for seeing the change, we sacrifice accuracy of each data point then in return for receiving details, so we accept messiness in return we get scale and trends. This isn’t means that the imprecision is unavoidable; on the contrary, we do develop methods to chase possible accuracy by collection, recording as well as management. For instance, Units of Measurement System was designed to solve these problems, which is to capture space, time and etc.
In language Translation System, the way Google does is testing algorithms with a trillion words, which means large also much messier dataset that come from the entire global Internet. The result turns out that the translation system runs better than any other systems else. In some fields, which can’t accept messiness like conventional sampling analysts, the way they deal with data is using multiple error-reducing strategies. In this way, specially trained experts collect samples according exact protocol, which is costly and long log. Thinking about not categorized content by tags for example; they are not well standardized and predefined. On the other hand, it makes the vastness of the Web’s content more navigable especially images, videos and music.
How to get access to data we need through such messy environment of Big Data seems require the Internet becomes more methodic. Since the birth of network science, people are tracing a more effective way to organize data on the Internet. The network science in some ways at first connected to physics, economics, biology, computer science, sociology and ecology. And the first documented mathematical analysis process is Euler’s (1783) analysis of the famous seven bridges crossing problem, which then he came up with the graph theory. And that was the first time a real space issue listed in an abstract term to show the bridges as links while isolated lands as resources. The development of graph theory dose influenced the principles of network science, because it established basic structure and form that interpret relationships between different links and resource on the Internet. There is no doubt that a well-structured network will considerably enhance the awareness of the hierarchy of resource. Graph drawing and network visualization are great helps for realize that target. And network visualization is under information visualization that employing elementary design principles, which aims at efficient and comprehensible representation of the targeted system. This will explores numerous phenomena such as technological networks, knowledge networks and biological networks. As a potential visual decoder of complexity, the practice is commonly driven by five key functions as: document, clarify, reveal, expand and abstract. In this case, the messy Big Data will promote its accuracy by being well structured.
Big Data has already shows its powerful aspects in the worldwide scale, and it dose provide possibilities for people to realize the arrangement of global resources as well as helps to the IoT better functioning.
In language Translation System, the way Google does is testing algorithms with a trillion words, which means large also much messier dataset that come from the entire global Internet. The result turns out that the translation system runs better than any other systems else. In some fields, which can’t accept messiness like conventional sampling analysts, the way they deal with data is using multiple error-reducing strategies. In this way, specially trained experts collect samples according exact protocol, which is costly and long log. Thinking about not categorized content by tags for example; they are not well standardized and predefined. On the other hand, it makes the vastness of the Web’s content more navigable especially images, videos and music.
How to get access to data we need through such messy environment of Big Data seems require the Internet becomes more methodic. Since the birth of network science, people are tracing a more effective way to organize data on the Internet. The network science in some ways at first connected to physics, economics, biology, computer science, sociology and ecology. And the first documented mathematical analysis process is Euler’s (1783) analysis of the famous seven bridges crossing problem, which then he came up with the graph theory. And that was the first time a real space issue listed in an abstract term to show the bridges as links while isolated lands as resources. The development of graph theory dose influenced the principles of network science, because it established basic structure and form that interpret relationships between different links and resource on the Internet. There is no doubt that a well-structured network will considerably enhance the awareness of the hierarchy of resource. Graph drawing and network visualization are great helps for realize that target. And network visualization is under information visualization that employing elementary design principles, which aims at efficient and comprehensible representation of the targeted system. This will explores numerous phenomena such as technological networks, knowledge networks and biological networks. As a potential visual decoder of complexity, the practice is commonly driven by five key functions as: document, clarify, reveal, expand and abstract. In this case, the messy Big Data will promote its accuracy by being well structured.
Big Data has already shows its powerful aspects in the worldwide scale, and it dose provide possibilities for people to realize the arrangement of global resources as well as helps to the IoT better functioning.
Euler’s (1783) analysis of the famous seven bridges crossing problem
