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　　Many researchers realize，Data has multiple explanations，How to distinguish the correct and incorrect explanation，To a certain extent, it determines the true and false of knowledge to generate knowledge，and this distinction is often the standard and practical significance。So，A data interpretation -based epistemology research began to enter the field of philosophy — the core content of data as the core content of epistemology research，Make a major challenge on the traditional philosophy of scientific knowledge。

New data collection、Storage、The emergence of analysis tools，Build the new discipline of big data science。Data produced in modern social life，It is the main research object of big data science。The emergence of big data science，It also provides new driving force for scientific research and social development。Current，Many scientific research depends on big data science to a certain extent。New data processing methods can improve the accuracy and prediction ability of scientific discovery，and help determine the future research direction。Scholars generally think，Big data has brought new data analysis technology and way of thinking to humans。but but，Some scholars believe that big data is only a tool for scientific research，Don’t recognize its unique historical status。The knowledge generated by the data set is reliability、Explanable and other aspects are questioned，It has triggered a challenge to the existing understanding of scientific knowledge。Exploring the knowledge of big data science，You can respond to this challenge，Bet365 lotto review You can also promote the development of the theory itself。

　　Method of the change of knowledge

Big Data Science，Also known as "Data -dense Science"，The main features are based on the volume (capacity) and cumulative speed of the data of significant growth。People understand big data，Although it originated from astronomy、Researchers in the fields of meteorology and other fields handle the history of large -scale complex data sets，But we cannot simply define "big data" with the size of the data capacity。James Nicholas Gray believes，Big Data Science is the fourth paradigm of scientific research。Sabina Leonelli thinks，Big data has cognitive power，Bridge can be set up between research groups using different methodological tools and theoretical frameworks (these groups are often decentralized because of concepts、Social barriers and technical problems cannot communicate with each other)。Big Data Science Requirement Mathematics、The combination of statistics and computer engineering and other skills，Develop a specific epistemology research。This episodes emphasize research methods (modeling、Statistics、Simulation, etc.) is an important driving force for research goals and output，It is also the main factor affecting the results of the research。but but，This episodes are essentially different from that of instrumentalism that only recognizes the meaning of data methodology。

Just as Galileo and Newton ’s mathematics have become the research trend of modern science after the mathematics of natural sciences，Data -based science has gradually become a new direction for contemporary research and development。This direction is closely related to the study of the concept of "data -driven" in computing sciences，and can defend the possibility of statistical probability of statistics。In data Bet365 lotto review -driven research，Researchers use the data set as the starting point of inductive reasoning，instead of relying on some of the theoretical "first -to -premature"。Some researchers think，This method is "the end of theory"。Traditional theoretical drive methods need to preset some "unshakable" theoretical principles，Data only plays the role of hypothetical inspection。Select data driver or theoretical driver paradigm，Decided different attitudes towards data sex。Knowledge generated under data drive，Most of them have related contacts，To find a more fundamental causal contact，Often it is more difficult -this is a bottom -up knowledge method，Essential dependence on inductive reasoning。Sumulatory reasoning itself，It has led to a deeper reflection -Is the way to reason from the data is effective? Does such knowledge have universal nature?

　　Facing reliability problems

With the gradual "data" with social life，Human activity is being subject to more and more monitoring and records，Great digital footprints produced。The monitoring equipment of "Potless" seems to record all human behavior in data，The large amount of data generated by this becomes a treasure trove of research。Extract knowledge from this type of data，People have developed increasingly complex computing tools。Big data science adopts novelty、Plan for efficient ways、Implement、Communication and evaluation research，Announce the way of knowledge generation，But at the same time caused the reliability of knowledge。

Traditional scientific knowledge is interpreted by logic empiricalism: the logical axiom system that is part of the interpretation -support of the reliability of knowledge is supported by the reliability of logical forms。Scientific theoretical semantic viewers explain scientific interpretations: a model set with the bet365 live casino games world -the reliability of knowledge is supported by models and representation of the reliability。In traditional epistemology，Knowing the subject's understanding of data is secondary，Discussion on data ontology and understanding is often "fine branches"。But as automation tools are more applied to processing of complex data，Should the machine be regarded as the subject of the new epistemology as the focus of research。Patrick Suppes) introduces statistical methods into philosophical research，Try to defend the accuracy of the accuracy of probability and logic for data promotion。Subsequent，Taking statistics as the starting point (mainly manifested as the introduction of the concept of correlation)，The main way to defend the rationality of the knowledge of big data reasoning。Bas C. Van Fraassen also agrees to summarize the frequency of data to establish a data model。Based on this，You can draw a conclusion: the better the data processing tool，The knowledge extracted from the data, the more reliable。But this conflict with the general understanding of knowledge -knowledge is not based on good or bad, but it is judged by true and false (such as "knowledge is the true conviction of defense")。So，Practical good or bad as a judgment standard for knowledge reliability in big data science，It is often a unwise choice。

Some researchers think，Data is an objective existence with real support，Obtaining knowledge from the data has an objective basis。The accumulation of knowledge in big data science is performed in the following ways: collect data through reliable methods，therefore produce a large number of "data type" facts available for analysis，These facts have some sense of correlation with other data，You can get more knowledge by digging this correlation to get more knowledge。but but，Many researchers bet365 live casino games realize，Data has multiple explanations，How to distinguish the correct and incorrect explanation，To a certain extent, it determines the true and false of knowledge to generate knowledge，and this distinction is often the standard and practical significance。So，A data interpretation -based epistemology research began to enter the field of philosophy — the core content of data as the core content of epistemology research，Make a major challenge on the traditional philosophy of scientific knowledge。

　Copy explanatory problems

Calculation Technology、Application of modeling tools and statistical methods，It brought us huge convenience。But at the same time，Big data has become a huge "mixed prize pool"，What "prizes" can you get，Often needs to rely on the advantages and disadvantages of the tool (such as，Supervision Learning、Model Fiting、Application of deep neural networks and search technology，Make data analysis technology an important tool for "drawing")。Roman Frigg and Julian Reiss believe，The simulation method in computational science does not generate new metaphysical school、Identification Theory、Semantics and methodology，No new philosophical problems are raised。Philosophy issues related to simulation are not specific in the field of simulation，Instead, most of the problems discussed in other contexts and their variants。So，They advocate，Computer simulation does not bring new problems in epistemology。Paul Humphreys opposes this view，He thinks，Calculation science "does not introduce any substantial new things to science，Actually ignoring the difference between practice and possible principles。

Looking at the entire process of scientific research on big data，There are two places that are more vague。One aspect，Human cognitive ability is limited，The complete understanding of the machine and data cannot be realized。On the other hand，After the machine intervention is intervened，Knowing the main object no longer has obvious boundaries。This caused an opaque problem of an unavoidable knowledge process，That is the explanatory problem of machine knowledge。Mathematics and calculation tools developed to analyze big data，It is usually opaque Bet365 lotto review for recognizing the subject。So，How should the credibility of the result be evaluated? How can the seemingly solid scientific building be built on the data knowledge of "shaking"?，The explanatory problem of knowledge needs to be solved urgently。Especially in the field of artificial intelligence, it seems in a prosperous scene，Smart progress in the real sense has not yet appeared，The prospect of the field of manual consciousness is also vague。This means，Knowing the philosophy of data and intelligence，Especially the study of data theory of data，You need to take the front of relevant scientific research。

Study on the Constitution of Big Data Science，It is the promotion of the essence of scientific knowledge in the perspective of philosophy。Given the huge success of modern science，The scientific and philosophical circles often look at scientific development with a more optimistic attitude，and less reflect on the essence of science、Knowledge and other basic problems，It is easy to pay enough attention to the problem of data -based knowledge -based understanding，This has laid hidden dangers for new scientific research characterized by computing。The vision of philosophy should not be limited to the surface analysis of calculations and data (such as attention to software and hardware iterative speeds、Analysis of the superiority of a certain type of algorithm，How to be credible as the foundation of the foundation as a scientific building。Study on the Constitution of Big Data Science，Need to take the reliability and explanatory issues of knowledge as the focus，New development of Popularization。This is not only a continuation of traditional epistemology，It is also a response to new requirements for the development of contemporary science and technology to philosophy，The foundation of the research on related Bet365 lotto review scientific and technological ethics issues。

　　(This article is the "Study on the Internal Theory of Big Data Personalized Knowledge and the Research on the Value of Epis. (18AZX008) staged results)

(Author Unit: School of Philosophy at Beijing Normal University)