Philosophy, Methodology and Paradigm Shift in Big Data

Baihaqi Siregar

and Muhammad Zarlis

Doctoral Program, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Indonesia

Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Indonesia

Keywords: big data, philosophy, methodology, paradigm shift

Abstract: Through information technology, such as social media, sensors, video surveillance or the intelligent network.

This ocean data produce a single Big Data terminology. Data plays an important role in strategic decision

making. Therefore, the parts that can process and use the data available in large, fast and different volumes

can certainly benefit greatly. The Big Data philosophy is a branch of philosophy with the basics, methods and

implications of Big Data involving large data sets that have a large volume, velocity, variety, value, and

veracity. The power of large data is in the analysis in which the results of the analysis are taken. Big data or

small data do not enter and in itself has value. This is only useful if we can get information from the data.

And this intuition can be used to combine our decisions. Along with Big Data, there is also a paradigm shift

in terms of analytical focus, from descriptive analysis to predictive and prescriptive analysis.

1 INTRODUCTION

Data are facts and statistics collected together for

reference or analysis. Information is a given or

learned fact about something or someone. The

Philosophy of Information is a new development with

a capacity to revolutionize philosophy and human

interactions with science, technology, data, and

reality. Both can be used as a basis for reasoning or

calculation. There are several opinions that define

data as facts and statistics, and information as

knowledge derived from facts and statistics. Kitchin

outlines the binary visions that the data itself is

neutral in terms of elements of reality or elements of

social construction like other elements. The position

of the data is neutral to reality without any policy or

agenda. Only people are interested in corrupt data and

direct it to their own interests, not the basic science

itself. Another point of view is that data are an

epistemological unit, socially constructed with all

prejudices, agendas and political forces that can be

incorporated into constructive social interests. As a

result, how the ontological data are defined are not

neutral, technical, but normative, political and ethical

processes. This type of known and common binary

vision exists for science in general, discussing

whether science is objective or requires subjectivity

because it is socially constructed. It would be difficult

to prove that science or data of biases from the

systems of thought and the tools at the base of their

production. He supports proposed the idea of the

required dataset by taking this explicitly different

influence, such as place, subjectivity, political

economy, institutions, rules and systems of thought.

Data science is the extraction of knowledge from

data. Data science covers large data and is intended

as a broader discipline that uses mathematical

engineering, theory, statistics, and information

technology such as machine learning, to discover data

models in which predictive models can be developed.

Intensive data processing requires a large set of data

sets that may require scientific computational

techniques for modelling, and observation of

complete dimensional data for experiments and can

be performed in distributed and copied networks.

2 GENERAL DEFINITIONS

Philosophy is the study of the nature of knowledge,

of truth, of reality and of existence. Its purpose is

largely composed in three general categories:

ontology, epistemology and ethics. Ontology refers to

existence; what are Big Data, how it looks, how it

works in the world; definitions and classifications.

Epistemology deals with knowledge; how Big Data

helps us learn about the new world, how real these

discoveries are, what the knowledge is needed to

Siregar, B. and Zarlis, M.

Philosophy, Methodology and Paradigm Shift in Big Data.

DOI: 10.5220/0010038900970100

In Proceedings of the 3rd International Conference of Computer, Environment, Agriculture, Social Science, Health Science, Engineering and Technology (ICEST 2018), pages 97-100

ISBN: 978-989-758-496-1

engage in the science of Big Data. Ethics discusses

how big data are made, how some types of results can

be obtained and why; Aesthetics is also part of this

category. Any philosophy should provide a concise

definition of what is and articulate its purpose and its

dimensions.

The philosophy of science is a branch of

philosophy that focuses on the foundations, methods

and implications of science. The main questions

about what science considers, the reliability of

scientific theory and the purpose of science. Various

data / information philosophy, Big Data, scientific

data and intensive data science can be understood in

this case as the basic structure, methods and

implications. Philosophy Data / Information is a

branch of philosophy that deals with the bases,

methods and implications of data and information;

existence, definition, conceptualization, method,

knowledge of possibilities, truth standards and

working practices with data and information. The Big

Data philosophy is a branch of philosophy that deals

with the basics, methods and implications of Big

Data; definition, intent, conceptualization, possible

knowledge, standards of truth and practice in

situations involving high volumes, high speed, large

amounts of information and data. Philosophy of data

science is the branch of philosophy that focuses on

the foundations, methods and implications of data

about science, the science of extracting knowledge

from data using techniques and the theory of

mathematics, statistics, and information technology.

The philosophy of intensive data science is a branch

of philosophy that deals with the basics, methods and

implications of intensive data science; Definition,

meaning, knowledge production, conceptualization

of science and discovery, definition of knowledge,

testing standards, and the practice of intensive

computational science in modelling situations,

observation, and a large-scale experiment.

3 PHILOSOPHIES IN BIG DATA

The Big Data philosophy concerns the basics,

methods and implications of Big Data; definitions,

meanings, conceptualizations, possible knowledge,

truth standards and practices in situations involving

large volume variety data sets at high speed. The

philosophy of the Big Data emerges as distinct and

different fields. The symbiotic productive

relationship between Big Data between scientists and

philosophers is necessary for further developments in

the field. The idea of Kant as a reference that

perception without conception is blind, and

conception without perception is empty. Different

types of blindness, conceptual and perceptive must be

investigated in the science of Big Data, for example

to avoid the case of fundamentalism data that blindly

trust the results of big data. Likewise, stresses that

such statistics, there is not a single version of the truth

in the science of Big Data. So we have to be critical

of the visualization of narrative data and data based

on history.

There is no consensus on how to define Big Data.

This term is often used as a synonym for related

concepts such as business intelligence and data

mining. It is true that these three terms concern data

analysis and in many cases advanced analysis. But the

concept of Big Data differs from two other factors

when the volume of data, the number of transactions

and the number of data sources are so large and

complex that they require special methods and

technologies to draw data. For example, traditional

data warehouse solutions may fail when dealing with

Big Data. Many parts try to define Big Data. Big Data

is a capacity, high capacity and / or diversified

information that requires the use of economic and

innovative information that enables better

understanding, decision making and process

automation. Everything refers to 3V: Volume,

Variety, Velocity, and some elements of Veracity and

Value:

 Volume refers to a very large or perhaps

unlimited size of data storage media;

 Variety data come from different data sources.

For the former, data can come from both

internal and external data sources. More

importantly, data can come in various formats

such as data tables, data structures and data

models, such as text, images, video streams,

audio reports and more. There is a shift from

individual structured data with unstructured

data or a combination of both;

 Velocity is associated with large amounts of

data about transactions with high refresh rates

that produce high-speed data streams and the

time to act on these data streams will often be

very short. There is a shift from batch

processing to real-time streaming;

 Meanwhile, the characteristics of Veracity and

Value are related to the uncertainty of the data

and the benefit value of the information

generated. In Big Data, data is too large and

too fast or incompatible with the conventional

database architecture structure. To get value

from data, technology should be used to

extract and obtain more specific information.

ICEST 2018 - 3rd International Conference of Computer, Environment, Agriculture, Social Science, Health Science, Engineering and

Technology

4 BIG DATA TERMINOLOGIES

Big Data terminology is often associated with data

science, data mining, and data processing. However,

Big Data involves more data mining infrastructures,

or data processing techniques than ever before. In

implementing Big Data technology in an

organization, there are 4 important elements that

become challenges, namely data, technology,

processes and human resources :

 Data;

The basic description of the data points to objects,

events, activities and transactions that are

documented, classified and stored but not set to give

a particular meaning. Data that has been organized

to give meaning and value to the recipient is called

information. Data availability is the key to Big Data

technology. There are many organizations that have

a lot of data about their business processes,

structured and unstructured data.

 Technology;

This is related to the infrastructure and tools in the

operation of Big Data, such as calculation and

analysis techniques, as well as media storage.

Normally, organizations will not suffer significant

limitations in technology because technology can

be acquired by buying or working with third parties.

 Process;

In the process of adopting Big Data technology, a

change in organizational culture is required. For

example, before Big Data, the leader in managing

the organization, make decisions based only on

intuition based on values, beliefs or hypotheses. But

after Big Data technology, leaders can act on data-

driven decisions means making decisions based on

accurate data and relevant information.

 Human resources.

When applying Big Data technology, human

resources must be able to analyse and create

creativity, that is, skills / competences to learn new

methods that can be collected to collect, interpret

and analyse data, computer programming skills and

commercial skills. to understand business goals.

Data sources in Big Data technology can be

structured and unstructured data. Structured data have

predefined data types, formats and structures. While

in unstructured data, text data with incorrect

formatting or no built-in structure, making it

structured to require more effort, tools and time. This

data is generated by internet applications such as

social media. Unstructured data sources are those

with little or no control over the format. Text data,

video data and audio data fall into this category.

Unstructured data is complicated because the

meaning of byte is not predetermined. Structured and

unstructured data include semi-structured data. Semi-

structured data are data that can be irregular or

incomplete and have structures that can change

quickly or unpredictably. It usually has a structure,

but does not fit the fixed pattern. Web logs are a good

example of semi-structured data.

5 METHODOLOGIES

Is it more important to work with Big Data than

traditional data? By reading large amounts of data, we

might start thinking that just because Big Data has a

high volume, speed and variations are somehow

better or more important than other data. This is not

the case. The power of large data is in the analysis in

which the results of the analysis are taken. Big data or

small data do not enter and in itself has value. This is

only useful if we can get information from the data.

And this intuition can be used to combine our

decisions. Since the introduction of Big Data in data

collection and analysis, this technique has been

compared with previously conducted conventional

methods, such as surveys. The comparison between

the two methods appears as follows :

Table 1. Comparison Method Between Conventional and

Data Analytics

Legacy Data Analytics

Confirmative Explorative (Predictive)

Small Data Set Large Data Set

Small Number of

Variable

Large Number of

Variable

Deductive

(No Predictions)

Inductive

Numeric Data Numeric and

Non-Numeric Data

Clean Data Data Cleaning

The phases of activities and technical support on

Big Data are as follows:

 Obtain, related resources and how to obtain

data;

 Access, related to the power of data access; the

data already collected require governance,

integration, storage and calculation to be

managed for the next step;

 Analytical, related to the information to be

obtained, to the results of the data

management that has been processed. The

analysis can be descriptive, diagnostic,

predictive (foresee future events) and

prescriptive (recommending options and

implications of each option);

 Application, related to the visualization and

results of analysis reports.

Philosophy, Methodology and Paradigm Shift in Big Data

There are several important ways in which Big

Data is different from traditional data sources. Frank

[10] suggests the following ways Big Data can be

viewed differently from traditional data sources.

First, Big Data can be a completely new source of

data. The transactions we perform are not transactions

that are fundamentally different from what we would

traditionally do. An organization can acquire Web

transactions, but the transaction is actually just the

same transaction that has been acquired from years.

However, it actually captures browsing behavior

because customers make transactions by creating new

fundamental data. Secondly, it can sometimes be

argued that the speed of data feeds increases so as to

qualify as a new source of data. Third, the more semi-

structured and unstructured data that arrive. Most

traditional data sources exist in structured areas.

6 PARADIGM SHIFT IN BIG DATA

Along with Big Data, there is also a paradigm shift in

terms of analytical focus. It is a passage from a

descriptive analysis to predictive and prescriptive

analysis. Descriptive analysis answers the question

"what happened in the past?" This usually involves

reporting. Predictive analysis aims at something

about "what could happen next?" This is more

difficult and involves the extrapolation of trends and

patterns in the future. While the prescriptive analysis

tries to answer, "how do I manage this"? This is where

the analysis starts to work. It's really corporate and

depends on the case. The three types of analysis

existed before the era of Big Data, but the focus was

on traditional reports. The difference that Big Data

brings to the table is an appetite and long-sighted

vision skills and appetite and ability for a quick and

usable vision.

7 CONCLUSIONS

The philosophy of the Big Data emerges as distinct

and different fields. The symbiotic productive

relationship between Big Data between scientists and

philosophers is necessary for further developments in

the field. The idea of Kant as a reference that

perception without conception is blind, and

conception without perception is empty. Different

types of blindness, conceptual and perceptive must be

investigated in the science of Big Data.

Big Data terminology is often associated with data

science, data mining, and data processing. But the

concept of Big Data is different from the others when

the volume of data, the number of transactions and the

number of data sources so large and complex that

requires special methods and technologies to attract

data. Traditional data storage solutions may fail when

dealing with Big Data.

By reading large amounts of data, we might start

thinking that just because Big Data has a high

volume, speed and variations are somehow better or

more important than other data. This is not the case.

The power of large data is in the analysis in which the

results of the analysis are taken. Big data or small data

do not enter and in itself has value. This is only useful

if we can get information from the data. And this

intuition can be used to combine our decisions.

Along with Big Data, there is also a paradigm shift

in terms of analytical focus, i.e. descriptive,

predictive and prescriptive analysis. The three types

of analysis existed before the era of Big Data, but the

focus was on traditional reports. The difference that

Big Data brings to the table is an appetite and long-

sighted vision skills and appetite and ability for a

quick and usable vision.

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Technology

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