Regarding Big Data through the Lens of the Philosophy of Information ^†

Abstract

We consider the philosophy of information as a meta-philosophy by providing an overview of it. Simultaneously, we investigate the origins, characteristics, goals, and changes in thinking of big data. On the basis of this, we study big data in the context of the philosophy of information, focusing on the limitations of big data and considering its essence as a means of processing the phenomena of the world, so as to avoid exaggerating the value and significance of big data, which would result in unnecessary energy and resource consumption.

1. Introduction

In the early 1980s, Professor Kun WU pioneered and established the Chinese philosophy of information. “The rise of the philosophy of information is a new synthesis achieved at the confluence of science and philosophy” [1], which responds to the information era’s call for a new philosophy, constructs a new worldview to explain the world, representing the philosophy of the era. Big data is a significant concept that has been proposed over the past few years as a collection of data that cannot be sensed, acquired, managed, processed, or supplied within a certain time using conventional machinery and software and hardware tools [2]. It is a novel phenomenon that emerged as a result of the evolution of information science and technology to the stage of cloud computing. Big data as an efficient method for processing vast quantities of data will have a profound effect on society. Big data is a method for dealing with the phenomena of the world, whereas the philosophy of information investigates the essence of the world. They are distinct from each other. However, despite their different heights and depths, both reflect characteristics of the information age. Consequently, it is necessary to investigate the philosophy of information and big data in the information age and to focus on big data in the context of the philosophy of information in order to gain a more objective understanding of big data, thereby accurately estimating its value and significance.

2. Overview of the Philosophy of Information

In the 1980s, the global revolution in information technology, the rise of the information economy, and the rise of the information society gave life to the Chinese philosophy of information. The comprehensive progress and development of the informatization of society and the socialization of information necessitated a new philosophy, which led to the emergence of the philosophy of information [1].

First and foremost, the philosophy of information is an effective response to the philosophical crisis of the information age. The revolution of information technology, the economy of information, the informationalization of science, the informationalization of society, and other shifts in numerous fields affect not only economic systems, political systems, cultural patterns, and ways of life, but also individuals’ ideas and thoughts. In this condition, the philosophy of information summarizes the influence of modern science on philosophy, and introduces the category of information, which leads to profound and essential advances in philosophy, into the construction of the philosophical system, and revises the philosophical category system. In a sense, philosophy’s crisis in the information age is partly solved.

Second, the philosophy of information has established a “matter-information” duality that is distinct from the traditional “matter-consciousness” duality in the realm of existence. This has resulted in a revolutionary shift at the level of philosophical ontology. Since this revolution is profoundly rooted in philosophical ontology, it will inevitably result in fundamental shifts in philosophical epistemology, philosophical evolution, philosophical socialism, philosophical value theory, philosophical thinking, philosophical methodology, etc., as well as all other philosophical fields. It is evident that the philosophy of information establishes its foundation as a meta- or primary philosophy [1].

Third, the philosophy of information also provides a new philosophical paradigm for the development of contemporary philosophy. The philosophy of information investigates information as a universalized form of existence, cognitive mode, value scale, and evolutionary principle, and consequently constructs a new ontology of information, epistemology of information, methodology of information, and evolution theory of information, etc.

In conclusion, the philosophy of information, as a type of new meta-philosophy, criticizes the concrete sciences and removes their limitations. In addition, the philosophy of information is a critique of philosophy itself, surpassing the old framework and theories of traditional philosophy in understanding the nature of information. The authentic philosophy of the information age is the philosophy of information.

3. Overview of Big Data

Big data is a novel phenomenon that emerged as a result of the development of information science and technology to the stage of cloud computing. It is a symbol of the phenomenal level of the information age. We analyze big data from the following aspects:

3.1. Sources and Characteristics of Big Data

Big data refers to a set of data that is so large and complex that it cannot be processed by current database management tools or data processing applications; its common characteristics are large scale (volume), velocity (speed), and diversity (variety) [3].

Depending on the source, big data can be broadly categorized as follows:

3.1.1. From People

Text, images, videos, and other information generated by individuals in the course of their Internet activities and mobile Internet usage;

3.1.2. Originating from Computers

Data generated by various types of computer information systems in the form of files, databases, multimedia, etc., and including automatically generated information such as audits and records;

3.1.3. From Things

Data gathered by a variety of digital devices. For instance, the digital signals generated by cameras, the various person-specific values generated by the medical Internet of Things, and the vast quantity of data generated by astronomical telescopes.

3.2. Goals of the Analysis of Big Data

Various disciplines, such as science, medicine, and business, use big data for different purposes, but their goals can be categorized as follows:

3.2.1. Acquiring Knowledge and Speculating Trends

Humans beings have a long history of acquiring and using knowledge through the analysis of data. Since big data contains a large amount of original and authentic information, analysis of big data can help people observe phenomena and comprehend the laws underlying things more precisely, and make more accurate predictions about natural or social phenomena.

3.2.2. Analyzing and Mastering Some Personalized Features

While individual activities share certain group characteristics, they also possess their own unique attributes. By accruing data over a long period of time and in multiple dimensions, businesses can analyze user’s patterns of behavior, depict individual profiles more accurately, and provide users with more individualized products and services, as well as more accurate advertising recommendations. For instance, Google analyzes the preferences of users to assist advertisers in evaluating the effectiveness of their advertising campaigns and estimating the size of the future market.

3.2.3. Discerning the Truth through the Analysis of Big Data

The negative effects of false information are greater than information deprivation, and so people are attempting to use big data to identify false information. Big data is supported by a large and diverse database, and the massive quantity of information can, to some extent, help to eliminate false information and retain the truth.

3.3. Changing People’s Thinking by Big Data

Big data represents a significant advancement in comprehending the world quantitatively, and it has had a significant impact on the perspective and approach to human comprehension of the world. From an epistemological standpoint, the analysis of big data has altered human cognition. This can be demonstrated in the following aspects:

First, in the selection of data, the all-data model is used, i.e., the total as a sample, emphasizing the aggregate effect of all data. Big data relies on enormous databases, eschewing the traditional method of random sample analysis. This represents a separation from the conventional method of analyzing data, as well as a shift in human thought.

Second, in the aspect of the effects of data analysis and processing, big data abandons the pursuit of precision and transforms into chaos. Due to the limited information collected in conventional data processing, minor errors are magnified and may even compromise the accuracy of the final result. Nonetheless, in the context of big data, the reorganization and expansion of numerous and diverse databases reduces the error for data analysis and processing. We obtain less-than-precise information, but the vast quantity of data collected makes it cost-effective to forego absolute precision. In summary, big data makes uncertainty and confusion more cost-effective than precision.

Third, in terms of interpreting the results of data analysis, big data abandons the exploration of causal relationships in favor of correlational analysis. Big data anticipates the probability of an event by correlating, or quantifying, the mathematical relationship between two data sets. Predictions based on the analysis of correlations are fundamental to big data.

By investigating the sources and characteristics of big data, the objectives of big data analysis, and the effects of big data, we have gained a more thorough understanding of big data and make greater use of it.

4. Examining Big Data from the Perspective of the Philosophy of Information

The philosophy of information is a critique of traditional philosophy; it represents a new turn in philosophy in response to the information age’s call for a new philosophy. As a meta-philosophy, the philosophy of information investigates the nature of the world using information as a medium and develops a new model for explaining the world. Big data is a technological breakthrough achieved by humans in the information age at the level of data analysis and processing, which has had a significant impact on many disciplines, including business and medicine, and has led to changes in the field of human thought.

The philosophy of information and big data are fundamentally distinct; the former is a meta-philosophy, whereas the latter is merely a technological method. Nonetheless, some academics compare big data to the philosophy of information and believe that the study of information philosophy should center on big data. This idea, according to the author, is open to dispute. Big data is a subfield of information science, and it cannot be elevated to the level of philosophy.

When we examine big data in the context of the philosophy of information, the limitations of big data are revealed, which manifest in the following aspects:

At the level of ontology, big data is a technical tool that does not explore the world’s nature but only process phenomena. This type of phenomenal data computation cannot reach the level of ontology. Consequently, big data can only exist as a technology and method in specific sciences and cannot be elevated to philosophical ontology. Any attempt to exaggerate the significance of big data or even use it as an ontological study of philosophy is therefore suspect.

On the level of methodology, big data analyzes and processes immense data to obtain information and knowledge through inductive methods, whereas phenomena are all-encompassing and the conclusions obtained through inductive methods are contingent; therefore, the processing of phenomena cannot rely on inductive methods. Big data is therefore methodologically unreliable and lacks a stable methodological basis.

Big data prioritizes the whole effect of aggregates of data and disregards individuality and contingency in the analysis and processing of data, which are not permitted by the philosophy of information. The philosophy aims to reconcile and unify universality and individuality, adhering to the principle of dialectics, whereas the approach of big data clearly departs from this philosophical principle, so it should not be elevated to the level of philosophy.

Big data values hybridity, but it is not a theory of complex systems. The theory of complex systems prioritizes a combination of holistic and reductionist approaches to system analysis. However, big data lacks a reductionist perspective and focuses exclusively on the utility of data aggregates.

5. Conclusions

In the information age, both the philosophy of information and big data have a significant impact. As a meta-philosophy, the philosophy of information critiques traditional philosophy, establishing a new philosophical system that includes information ontology, information epistemology, information production theory, information social theory, information value theory, information methodology, information evolution theory, and so on. Despite the fact that big data is merely a method of data analysis and processing in the information age, it has had a significant impact on many facets of society, despite its many flaws. First, on an ontological level, big data is a technical tool that does not explore the essence of the world but rather processes the phenomena of the world. Second, on a methodological level, big data is founded on the inductive principle, which determines its unreliability. Third, big data departs from the philosophical principle of the unification of individuality and generality. Fourth, big data values hybridity but is not a complex system theory. Therefore, it is unnecessary to elevate the investigation and study of big data to a philosophical level. Big data is a fad, whereas the philosophy of information reflects the standardization of human society. Exploring big data in the context of the philosophy of information is conducive to enhancing our understanding of it so as to avoid exaggerating the value and significance of big data, and wasting energy and resources.