WO2018157332A1

WO2018157332A1 - Statistical method and system applied to big data

Info

Publication number: WO2018157332A1
Application number: PCT/CN2017/075333
Authority: WO
Inventors: 马岩
Original assignee: 深圳市博信诺达经贸咨询有限公司
Priority date: 2017-03-01
Filing date: 2017-03-01
Publication date: 2018-09-07

Abstract

A statistical method applied to big data, comprising the following steps: a server obtains searching times and extraction times of big data (101); the server records the searching times of the big data and the type of the server that extracts the big data (102); the server establishes a statistic list, the statistic list comprising the searching times, the extraction times, and an identification of the big data (103). The method has an advantage of decreasing the searching times.

Description

Statistical method and system applied to big data

Technical field

The present invention relates to the field of data processing, and in particular, to a statistical method and system for applying to big data.

Background technique

The strategic significance of big data technology is not to master huge data information, but to professionalize these meaningful data. In other words, if big data is likened to an industry, the key to profitability in this industry is to increase the “processing capability” of the data and “add value” of the data through “processing”. From a technical point of view, the relationship between big data and cloud computing is as inseparable as the front and back of a coin. Big data must not be processed by a single computer, and a distributed architecture must be used. It features distributed data mining for massive data. But it must rely on cloud computing for distributed processing, distributed databases and cloud storage, and virtualization technologies. With the advent of the cloud era, big data (Big Data) has also attracted more and more attention. The team of analysts at Yuntai believes that Big Data (Big) Data) is often used to describe a large amount of unstructured data and semi-structured data created by a company that spends too much time and money when downloaded to a relational database for analysis. Big data analytics is often associated with cloud computing because real-time large dataset analysis requires a framework like MapReduce to distribute work to dozens, hundreds, or even thousands of computers.

Existing big data does not count big data, resulting in more frequent searches.

technical problem

The present application provides a statistical method for applying to big data. It solves the shortcomings of the prior art technical solution that the number of repeated searches is large.

Technical solution

In one aspect, a statistical method for applying to big data is provided, the method comprising the following steps: applying a statistical method to big data, the method comprising the following steps:

The number of times the server retrieves big data and the number of extractions;

The number of searches the server records for big data and the type of server that extracts the big data;

The server establishes a statistics list, which includes: the number of searches, the number of extractions, and the identity of big data.

Optionally, the method further includes:

The server adds the searched keywords to the statistics list.

Optionally, the method further includes:

If the server receives the search keyword, the big data identifier of the search keyword is sent to the search device.

In a second aspect, a statistical system for applying to big data is provided, the system comprising:

An acquisition unit for obtaining the number of searches for big data and the number of extractions;

The processing unit is configured to record the number of searches of the big data and the type of the server that extracts the big data, and establish a statistics list, where the statistics list includes: the number of searches, the number of extractions, and the identifier of the big data.

Optionally, the system further includes:

A processing unit for the server to add the searched keywords to the statistical list.

Optionally, the system further includes:

The processing unit is configured to send the big data identifier of the search keyword to the search device if the search keyword is received.

A third aspect provides a server, including: a processor, a wireless transceiver, a memory, and a bus, wherein the processor, the wireless transceiver, and the memory are connected by a bus, and the wireless transceiver is configured to obtain a search count of big data. And the number of extractions;

The processor is configured to record the number of searches of the big data and the type of the server that extracts the big data, and establish a statistics list, where the statistics list includes: the number of searches, the number of extractions, and the identifier of the big data.

Optionally, the processor is configured to add a searched keyword to the statistics list by the server.

Optionally, the processor is configured to send the big data identifier of the search keyword to the search device, if the search keyword is received.

Beneficial effect

The technical solution provided by the present invention establishes a statistical list of big data, so it has the advantage of reducing the number of big data searches.

DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are some embodiments of the present invention. Those skilled in the art can also obtain other drawings based on these drawings without paying any creative work.

1 is a flowchart of a statistical method applied to big data according to a first preferred embodiment of the present invention;

2 is a structural diagram of a statistical system applied to big data according to a second preferred embodiment of the present invention.

FIG. 3 is a hardware structural diagram of a server according to a second preferred embodiment of the present invention.

Embodiments of the invention

The technical solutions in the embodiments of the present invention are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present invention. It is obvious that the described embodiments are a part of the embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts are within the scope of the present invention.

Please refer to FIG. 1. FIG. 1 is a statistical method applied to big data according to a first preferred embodiment of the present invention. The method is as shown in FIG. 1 and includes the following steps:

Step S101: The server obtains the number of searches of big data and the number of extractions.

Step S102: The server records the number of searches of big data and the type of the server that extracts the big data.

Step S103: The server establishes a statistics list, where the statistics list includes: the number of searches, the number of extractions, and the identifier of the big data.

Optionally, the server adds the searched keywords to the statistics list.

Optionally, if the server receives the search keyword, the server sends the big data identifier of the search keyword to the search device.

Please refer to FIG. 2. FIG. 2 is a statistical system applied to big data according to a second preferred embodiment of the present invention. The system is as shown in FIG. 2, and includes:

The obtaining unit 201 is configured to acquire the number of searches of the big data and the number of times of extraction;

The processing unit 202 is configured to record the number of searches of the big data and the type of the server that extracts the big data, and establish a statistics list, where the statistics list includes: the number of searches, the number of extractions, and the identifier of the big data.

Optionally, the processing unit 202 is configured to add, by the server, the searched keywords in the statistics list.

Optionally, the processing unit 202 is configured to send the big data identifier of the search keyword to the search device, if the search keyword is received.

Referring to FIG. 3, FIG. 3 is a server 30, including: a processor 301, a wireless transceiver 302, a memory 303, and a bus 304. The wireless transceiver 302 is configured to send and receive data with and from an external device. The number of processors 301 can be one or more. In some embodiments of the present application, processor 301, memory 302, and transceiver 303 may be connected by bus 304 or other means. Server 30 can be used to perform the steps of FIG. For the meaning and examples of the terms involved in the embodiment, reference may be made to the corresponding embodiment of FIG. 1. I will not repeat them here.

The wireless transceiver 302 is configured to acquire the number of searches of big data and the number of extractions.

The program code is stored in the memory 303. The processor 901 is configured to call the program code stored in the memory 903 for performing the following operations:

The processor 301 is configured to record the number of searches of the big data and the type of the server that extracts the big data, and establish a statistics list, where the statistics list includes: the number of searches, the number of extractions, and the identifier of the big data.

It should be noted that the processor 301 herein may be a processing component or a general term of multiple processing components. For example, the processing element can be a central processor (Central) Processing Unit, CPU), or a specific integrated circuit (Application Specific Integrated) Circuit, ASIC), or one or more integrated circuits configured to implement embodiments of the present application, such as one or more microprocessors (digital singnal Processor, DSP), or one or more Field Programmable Gate Arrays (FPGAs).

The memory 303 may be a storage device or a collective name of a plurality of storage elements, and is used to store executable program code or parameters, data, and the like required for the application running device to operate. And the memory 303 may include random access memory (RAM), and may also include non-volatile memory (non-volatile memory) Memory), such as disk storage, flash (Flash), etc.

Bus 304 can be an industry standard architecture (Industry Standard Architecture, ISA) bus, Peripheral Component (PCI) bus or extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, only one thick line is shown in Figure 3, but it does not mean that there is only one bus or one type of bus.

The terminal may further include input and output means connected to the bus 304 for connection to other parts such as the processor 301 via the bus. The input/output device can provide an input interface for the operator, so that the operator can select the control item through the input interface, and can also be other interfaces through which other devices can be externally connected.

It should be noted that, for the foregoing various method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should understand that the present invention is not limited by the described action sequence. Because certain steps may be performed in other sequences or concurrently in accordance with the present invention. In addition, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

In the above embodiments, the descriptions of the various embodiments are different, and the parts that are not described in detail in a certain embodiment can be referred to the related descriptions of other embodiments.

A person skilled in the art may understand that all or part of the various steps of the foregoing embodiments may be performed by a program to instruct related hardware. The program may be stored in a computer readable storage medium, and the storage medium may include: Flash drive, read-only memory (English: Read-Only Memory, referred to as: ROM), random accessor (English: Random Access Memory, referred to as: RAM), disk or CD.

The content downloading method and the related device and system provided by the embodiments of the present invention are described in detail above. The principles and implementation manners of the present invention are described in the specific examples. The description of the above embodiments is only used to help understand the present invention. The method of the invention and its core idea; at the same time, for the person of ordinary skill in the art, according to the idea of the present invention, there are some changes in the specific embodiment and the scope of application. In summary, the content of the specification should not be understood. To limit the invention.

Claims

A statistical method applied to big data, characterized in that the method comprises the following steps:

The number of times the server retrieves big data and the number of extractions;

The number of searches the server records for big data and the type of server that extracts the big data;

The server establishes a statistics list, which includes: the number of searches, the number of extractions, and the identity of big data.
The method of claim 1 further comprising:

The server adds the searched keywords to the statistics list.
The method of claim 2, wherein the method further comprises:

If the server receives the search keyword, the big data identifier of the search keyword is sent to the search device.
A statistical system for applying to big data, characterized in that the system comprises:

An acquisition unit for obtaining the number of searches for big data and the number of extractions;

The processing unit is configured to record the number of searches of the big data and the type of the server that extracts the big data, and establish a statistics list, where the statistics list includes: the number of searches, the number of extractions, and the identifier of the big data.
The system of claim 4, wherein the system further comprises:

A processing unit for the server to add the searched keywords to the statistical list.
The system of claim 5, wherein the system further comprises:

The processing unit is configured to send the big data identifier of the search keyword to the search device if the search keyword is received.
A server includes: a processor, a wireless transceiver, a memory, and a bus, wherein the processor, the wireless transceiver, and the memory are connected by a bus, wherein

The wireless transceiver is configured to acquire the number of searches of big data and the number of extractions;

The processor is configured to record the number of searches of the big data and the type of the server that extracts the big data, and establish a statistics list, where the statistics list includes: the number of searches, the number of extractions, and the identifier of the big data.
The server according to claim 7, wherein the processor is configured to add a searched keyword to the statistical list by the server.
The server according to claim 7, wherein the processor is configured to send the big data identifier of the search keyword to the search device if the search keyword is received.