Time sequence-based sorting method and apparatus

Abstract

Embodiments of the present invention provide a time sequence-based sorting method and apparatus. In the embodiments of the present application, to solve the problem in the prior art of "poor effect of timeliness-based sorting", the present application provides a solution of "sorting on the basis of associated data of context-aware vectors, an encoding mechanism for sorting the vectors in a time sequence, and a self-adaptive interval", specifically comprising: acquiring a data set; determining associated data attribute values of target data according to the data set, and determining time codes of the target data according to the associated data attribute values; and performing timeliness-based sorting on data in the data set according to the time codes. According to the context-aware vector representation method of the present application, the technical problem of "poor effect of timeliness-based sorting" is solved, and the context information of related attributes is also incorporated while achieving vectorized representation of target attributes, so that the timeliness of the target attributes can be accurately determined.

Description

A time sequence sorting method and device Technical Field

[0001] The present invention relates to the field of data identification, and in particular to a time sequence sorting method and device. Background Art

[0002] Data in real life is constantly changing. According to the Royal Mail, every day in the UK, 9,590 households move, 1,496 get married, 810 divorce, 2,011 retire, and 1,500 people die. It's estimated that inaccurate customer data can cost companies 6% of their annual revenue. When search engine data is outdated, a restaurant search may return a restaurant that's been closed for three years. When data on the condition of infrastructure assets is outdated, it can cause equipment repair delays and downtime. Furthermore, data-driven decisions based on outdated data can be worse than decisions made without the data at all. This is especially true in critical industries like healthcare, retail, and financial services, where it's impossible to make good decisions based on yesterday's data. Unfortunately, 82% of companies make decisions based on outdated information. These issues highlight the need to determine the freshness of data. In other words, we need to be able to determine how fresh the data is.

[0003] Existing models can be used to solve the problem of data timeliness sorting.

[0004] However, some unique characteristics of time series have not been well considered and optimized in existing models. Therefore, directly using existing models to sort data by timeliness has a poor effect.

[0005] Summary of the Invention

[0006] In view of the above problems, the present application is proposed to provide a timing sequencing method and apparatus that overcomes the above problems or at least partially solves the above problems, including:

[0007] A time series sorting method, for sorting data by timeliness, comprising:

[0008] Acquire a data set, the data set comprising data and attribute values ​​corresponding thereto; wherein the data set comprises at least two groups of data;

[0009] Determining an associated data attribute value of the target data according to the data set, and determining a time code of the target data according to the associated data attribute value;

[0010] The data in the data set are sorted by timeliness according to the time code.

[0011] Preferably, the step of determining the associated data attribute value of the target data based on the data set includes:

[0012] Determining the pre-data of the target data, the post-data of the target data, and the target data according to the data set;

[0013] The associated data attribute value of the target data is determined according to the preceding data of the target data, the following data of the target data, the target data, and the data set.

[0014] Preferably, the step of determining the time code of the target data according to the attribute value of the associated data includes:

[0015] Determining an attribute vector of each target data according to the attribute value of the associated data;

[0016] The time code of each target data is determined according to the attribute vector of each target data.

[0017] Preferably, the step of determining the attribute vector of each target data according to the attribute value of the associated data includes:

[0018] Determining a related data sequence according to the related data attribute value;

[0019] An attribute vector of each target data is determined according to the associated data sequence.

[0020] Preferably, the step of determining the time encoding of each target data according to the attribute vector of each target data comprises:

[0021] Determining a numerical code according to the attribute vector of each target data;

[0022] Determining an attribute code according to the attribute vector of each target data;

[0023] The time code of each target data is determined according to the attribute code and the value code.

[0024] Preferably, the step of determining the time code of each target data according to the attribute code and the numerical code includes:

[0025] A time code of each target data with an adaptive interval is obtained through a loss function according to the attribute code and the value code.

[0026] Preferably, the step of sorting the data in the data set by timeliness according to the time code comprises:

[0027] The data in the data set are sorted in chronological order according to the time code.

[0028] To implement this application, a time sequence sorting device is also provided. The device is used to sort data according to timeliness, including:

[0029] An acquisition module, configured to acquire a data set, wherein the data set includes data and attribute values ​​corresponding thereto; wherein the data set includes at least two groups of data;

[0030] a time coding module, configured to determine an associated data attribute value of target data according to the data set, and determine a time coding of the target data according to the associated data attribute value;

[0031] The timeliness sorting module is used to sort the data in the data set according to the time code.

[0032] To implement the present application, a computer electronic device is also provided, comprising a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the steps of the timing sorting method when executed by the processor.

[0033] To implement the present application, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the timing sorting method are implemented.

[0034] This application has the following advantages:

[0035] In the embodiments of the present application, compared to the "poor effect of timeliness sorting" in the prior art, the present application provides a solution of "context-aware vector association data, an encoding mechanism for sorting vectors in time order, and adaptive interval sorting", specifically: obtaining a data set, the data set containing data and attribute values ​​corresponding thereto; wherein the data set includes at least two groups of data; determining the associated data attribute values ​​of the target data based on the data set, and determining the time coding of the target data based on the associated data attribute values; and sorting the data in the data set based on the time coding. The technical problem of "poor effect of timeliness sorting" is solved by the context-aware vector representation method of the present application, so that the context information of the related attributes is incorporated while the target attributes are vectorized, thereby accurately judging the timeliness of the target attributes. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for the description of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0037] FIG1 is a flowchart of a timing sorting method according to an embodiment of the present application;

[0038] FIG2 is a module structure diagram of a timing sorting method provided in an embodiment of the present application;

[0039] FIG3 is a data set diagram of a time sequence sorting method provided by an embodiment of the present application;

[0040] FIG4 is a structural block diagram of a timing sequencing device provided by an embodiment of the present application;

[0041] FIG5 is a schematic structural diagram of a computer device provided by an embodiment of the present invention;

[0042] 12. Computer device; 14. External device; 16. Processing unit; 18. Bus; 20. Network adapter; 22. I / O interface; 24. Display; 28. Memory; 30. Random access memory; 32. Cache memory; 34. Storage system; 40. Program / utility; 42. Program module. DETAILED DESCRIPTION

[0043] To make the objectives, features, and advantages of this application more readily apparent, the present application is further described below in conjunction with the accompanying drawings and specific embodiments. It is apparent that the embodiments described are only a portion of the embodiments of this application, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments in this application without inventive effort are also within the scope of protection of this application.

[0044] By analyzing existing technologies, the inventors discovered that: As shown in Figure 3, which shows an image of a dataset, consider a set of records belonging to the same entity. Their attribute values ​​may be outdated and inaccurate, and only partially reliable timestamps may be available. In this case, how can we determine the recency of data attributes? In other words, given two records t1 and t2 belonging to the same entity, we need to determine whether the value of attribute A in t1 is newer than the value of attribute A in t2, expressed as t2 < A t1.

[0045] Consider the customer records t1-t6 shown in Figure 3, which have been confirmed to refer to the same person, Mary. Each record records some attribute values ​​of her marital status, job, number of children, and SZ (shoe size). The attributes of some of these records are outdated. For example, her job, address, and last name have changed 4 times, 5 times, and 2 times respectively. But only some attribute values ​​may have reliable timestamps. For example, the timestamps of t5 [job] and t6 [job] may be 2016 and 2019 respectively, indicating that at that time, these attribute values ​​were the latest. However, in the absence of complete timestamps, it is difficult to know whether t2 < LN t6, that is, the last name value of t2 is negated and the last name value of t6 is new. In addition, questions such as what is Mary's latest job need to be sorted.

[0046] Existing technologies usually learn a ranking model so that we can sort data based on learned relevance, preference, or importance. In our problem, we sort based on timeliness. Existing methods can use pairwise sorting. This is because the semantics of pairwise sorting and the semantics of temporal order are consistent. They can both determine whether the value of attribute A of t1 is newer than the value of attribute A of t2 given a pair of records t1 and t2 belonging to the same entity. In addition, temporal order has transitive properties, that is, if t2 < A t1 and t3< A t2, then we can deduce t3< A Therefore, pairwise sorting can help us obtain a total order of all attribute values, thereby obtaining the latest value on each attribute.

[0047] People naturally hope to use existing models to solve the problem of time-sensitive data sorting. However, because some unique characteristics of time series are not well considered and optimized in existing models, directly using existing models to sort data by timeliness is ineffective. There are three specific reasons for this:

[0048] Attribute relevance. When determining the timeliness order of data, we often need to refer to other attributes to determine the timeliness order of a given attribute. Furthermore, attribute values ​​can change back and forth. For example, Mary's marital status might change from "married" to "divorced" and then back to "married." Therefore, it's difficult to determine the most recent value of a single attribute based solely on information about that attribute.

[0049] Limitations of embedding models. To determine the timeliness of data, we need to be particularly careful with attribute values ​​that are lexically different but semantically similar. For example, in a person's status, "dead" and "expired" have similar meanings, and "married" and "married" also have similar meanings. Although existing embedding models (such as ELMo or BERT) are widely used to abstract semantic information, they cannot be directly used in timeliness problems because they have not been trained on chronologically ordered data.

[0050] Adaptive intervals. Existing ranking strategies don't account for the timeliness of data attributes in real life. For example, the time span from "birth" to "engagement" is typically longer than the time span from "engagement" to "marriage." Most existing strategies use a fixed interval approach for ranking. However, we need an adaptive interval method that can account for the timeliness of data and integrate the ranking results with real-life behavior to demonstrate the effectiveness of the ranking results.

[0051] It should be noted that, in any embodiment of the present invention, the method is used to sort data by timeliness.

[0052] 1 , a flowchart of a timing sorting method according to an embodiment of the present application is shown, which specifically includes the following steps:

[0053] S110, obtaining a data set, wherein the data set includes data and attribute values ​​corresponding thereto; wherein the data set includes at least two groups of data;

[0054] S120, determining an associated data attribute value of the target data according to the data set, and determining a time code of the target data according to the associated data attribute value;

[0055] S130: Sort the data in the data set by timeliness according to the time code.

[0056] In the embodiments of the present application, compared to the "poor effect of timeliness sorting" in the prior art, the present application provides a solution of "context-aware vector association data, an encoding mechanism for sorting vectors in time order, and adaptive interval sorting", specifically: obtaining a data set, the data set containing data and attribute values ​​corresponding thereto; wherein the data set includes at least two groups of data; determining the associated data attribute values ​​of the target data based on the data set, and determining the time coding of the target data based on the associated data attribute values; and sorting the data in the data set based on the time coding. The technical problem of "poor effect of timeliness sorting" is solved by the context-aware vector representation method of the present application, so that the context information of the related attributes is incorporated while the target attributes are vectorized, thereby accurately judging the timeliness of the target attributes.

[0057] Next, the time sequence sorting method in this exemplary embodiment will be further described.

[0058] As described in the above step S110, a data set is obtained, wherein the data set includes data and attribute values ​​corresponding thereto in a one-to-one manner; wherein the data set includes at least two groups of data.

[0059] In one embodiment of the present invention, the specific process of "obtaining a data set, wherein the data set includes data and attribute values ​​corresponding thereto in a one-to-one manner; wherein the data set includes at least two groups of data" in step S110 can be further explained in combination with the following description.

[0060] In a specific embodiment, as shown in FIG3 , given two records t1 and t2 belonging to the same entity, we need to determine whether the A attribute value of t1 is newer than the A attribute value of t2 , which is expressed as t2 < A t1.

[0061] As described in step S120 above, the associated data attribute value of the target data is determined according to the data set, and the time code of the target data is determined according to the associated data attribute value.

[0062] In one embodiment of the present invention, the specific process of "determining the associated data attribute value of the target data according to the data set, and determining the time code of the target data according to the associated data attribute value" in step S120 can be further explained in combination with the following description.

[0063] As described in the following steps, the target data's pre-data, target data's post-data and target data are determined based on the data set; and the associated data attribute value of the target data is determined based on the target data's pre-data, target data's post-data, target data and the data set.

[0064] In a specific embodiment, the preceding data is the preceding data, and the following data is the following data.

[0065] As shown in the following steps, an attribute vector of each target data is determined according to the attribute value of the associated data; and a time code of each target data is determined according to the attribute vector of each target data.

[0066] In one embodiment of the present invention, the specific process of the step of "determining the pre-data of the target data, the post-data of the target data, and the target data based on the data set; determining the associated data attribute value of the target data based on the pre-data of the target data, the post-data of the target data, the target data, and the data set" can be further explained in combination with the following description.

[0067] In one specific embodiment, the data has attribute correlations. When determining the timeliness order of data, we often need to refer to other attributes to determine the timeliness order of a given attribute. Furthermore, attribute values ​​in data may change back and forth. For example, Mary's marital status may change from "married" to "divorced" and then back to "married." Therefore, it is difficult to determine the most recent value of a single attribute based solely on the information on that attribute.

[0068] As described in the following steps, pre-data and post-data are established for each target data. In the data set, the pre-data of the target data is matched with the corresponding pre-data attribute value, the post-data of the target data is matched with the corresponding post-data attribute value, and the target data attribute value corresponding to the target data. The associated data attribute value includes the pre-data attribute value, the post-data attribute value, and the target data attribute value.

[0069] In an embodiment of the present invention, the specific process of the step of "determining the time code of the target data according to the attribute value of the associated data" can be further explained in combination with the following description.

[0070] As described in the following steps, an attribute vector of each target data is determined according to the attribute value of the associated data; and a time code of each target data is determined according to the attribute vector of each target data.

[0071] In one embodiment of the present invention, the specific process of the step of "determining the attribute vector of each target data according to the attribute value of the associated data" can be further explained in combination with the following description.

[0072] As an example, the ranking model Mrank first uses a pre-trained language model (such as ELMo or Bert) to establish a vector representation with contextual information for each attribute value, so that the attribute value itself and the attribute value information related to the attribute are also embedded.

[0073] As described in the following steps, a related data sequence is determined according to the attribute value of the related data; and an attribute vector of each target data is determined according to the related data sequence.

[0074] In a specific embodiment, in order to reference the associated data attribute values ​​related to the target data, we treat each record as a sequence and adopt the serialization concept so that the information in the record can be effectively digested by the model and embedded into the vector. Specifically, given a record t, we serialize its value as follows: serialize(t) = A1 <val>t[A1]...A n <val>t[A n ],

[0075] Among them and <val>are special identifiers that indicate the beginning of an attribute and an attribute value, respectively. The serialization of t is passed as input to a pre-trained language model emb(), which calculates a d-dimensional embedded vector for each A attribute value of t, represented by emb(t[A]). In addition, we take the average of all attribute embedding vectors to obtain a contextual representation of record t. Finally, for each attribute value of A in record t, we can get a context-aware vector representation: E t[A] =[emb(t[A]);emb(t)],

[0076] Among them, [;] represents the concatenation of vectors. According to this strategy, the numerical vector E t[A] It not only represents the numerical information of attribute A, but also contains the contextual information of other related attributes, which makes it easier for us to sort the values ​​of attribute A in a timely manner. Similar to numerical vectors, we can also vectorize attribute names. Specifically, we pass the attribute name A (such as "marital status") into the pre-trained language model to obtain an attribute vector E. A =emb(A).

[0077] In one embodiment of the present invention, the specific process of the step of "determining the time code of each target data according to the attribute vector of each target data" can be further explained in combination with the following description.

[0078] As described in the following steps, a numerical code is determined according to the attribute vector of each target data; an attribute code is determined according to the attribute vector of each target data; and a time code of each target data is determined according to the attribute code and the numerical code.

[0079] As an example, based on the constructed vector representation, McCrank encodes each attribute name A to obtain a target code. Similarly, for each attribute value t[A], we also encode it into a numerical code. The closer the numerical code of t[A] is to the target code of attribute A, the more recent the attribute value of t[A] is. Furthermore, the distance between the numerical code of t[A] and the target code of attribute A reflects the time span between them in the temporal ranking, as shown in Figure 2.

[0080] In a specific embodiment, although the methods of vectorized representation based on pre-trained language models have been widely used to capture semantic information, they have not been specifically used for time-sensitive sorting. Therefore, we adopt a chronological encoding method to reorganize the vectorized representation obtained based on the language model to maintain timeliness. The specific idea is to use the attribute vector as the target vector so that the numerical vector corresponding to the updated numerical value is closer to the target vector; in addition, unlike most existing sorting methods that adopt fixed intervals, we make the target encoding and the numerical encoding have an adaptive interval, and the interval is consistent with the time span in the time-sensitive sorting.

[0081] Specifically, given the numeric vector E of attribute A of record t t[A] , we encode it using the context encoder ENCcxtx(·) as follows: φ t[A] =ENC cxtx (E t[A] )=σ(W2*σ(W1*E t[A] )),

[0082] Where W1 and W2 are learnable parameters of the encoder, and σ is the sigmoid activation function, i.e.

[0083] Similarly, for the attribute vector E of attribute A A For example, we can also encode it through an attribute encoder ENCattr(·) as follows: A =ENC attr (E A ),

[0084] In order to train an encoder with temporal coding properties and adaptive intervals, we adopt an attribute-centered adaptive interval loss function. Given an attribute A, its loss function is:

[0085] Among them, <·,·> represents vector dot product, γ t1,t2 is the adaptive interval between records t1 and t2. Usually, we set γ t1,t2 for:

[0086] This plots the frequency with which the two values ​​t1[A] and t2[A] appear together.

[0087] Intuitively, for each training sample t1< A For t2, by minimizing this loss function, we are making our encoder achieve the following effects: (a) the attribute encoding of the relatively updated value t2[A] is closer to the target encoding, and (b) the attribute value encodings of t1[A] and t2[A] have an adaptive interval.

[0088] In other words, the attribute values ​​in the encoding space are not only arranged in chronological order according to their "distance" to the target encoding, which makes it easy to deduce the temporal order of the attributes, but the intervals between the corresponding encodings are also adaptively determined (not fixed), thus reflecting the semantics of temporal sorting.

[0089] In one embodiment of the present invention, the specific process of the step of "determining the time code of each target data according to the attribute code and the numerical code" can be further explained in combination with the following description.

[0090] As described in the following steps, a time code of each target data with an adaptive interval is obtained based on the attribute code and the numerical code through a loss function; wherein the adaptive interval refers to the distance between the code of each target data attribute value arranged in time sequence and the code of the preset data attribute value.

[0091] As described in the above step S130, the data in the data set are sorted by timeliness according to the time code.

[0092] In one embodiment of the present invention, the specific process of "sorting the data in the data set by timeliness according to the time code" in step S130 may be further explained in conjunction with the following description.

[0093] As described in the following steps, the data in the data set are sorted in chronological order according to the time code.

[0094] As an example, based on the distances of all numerical codes to the target code, we can deduce the timeliness ranking of the values ​​under this attribute.

[0095] In a specific embodiment, this application proposes a new data timeliness ranking model to address the shortcomings of existing technologies. Its key features and novelties include: a context-aware vector representation method that can simultaneously vectorize the target attribute value and its related attribute values; an encoding mechanism that can sort vectors in chronological order; and an attribute-centric adaptive interval sorting strategy.

[0096] In a specific embodiment, first, when determining the timeliness order of data, we often need to refer to other attributes to determine the timeliness order of a given attribute. The vector representation obtained by existing embedding models is usually based on a single attribute, and therefore cannot integrate relevant information on other attributes. In contrast, the present invention proposes a new context-aware vector representation method that not only vectorizes the target attribute but also incorporates the contextual information of related attributes, thereby accurately determining the timeliness of the target attribute.

[0097] Secondly, while existing embedding models are widely used to abstract semantic information and can handle lexically different but semantically similar attribute values, they cannot chronologically sort the attributes. Therefore, this paper proposes a new encoding mechanism that chronologically arranges the "distance" between the encoding of each attribute value and the target encoding, making it easy to deduce the temporal order of the attributes.

[0098] Finally, existing sorting strategies fail to account for the real-world timeliness of data attributes and instead employ fixed-interval sorting. However, in time-sensitive sorting, the time spans between attributes vary. Therefore, we propose an adaptive interval method that accounts for data timeliness. Different intervals are used between different attributes, ensuring that the sorting results align with the real-world behavior of data attributes.

[0099] In a specific embodiment, we verified the effectiveness of the present invention through experiments on real datasets. We implemented the ranking model Mrank and compared the effects of three schemes: one that does not use a context-aware vector representation method, one that does not use a temporal encoding mechanism, and one that does not use an adaptive interval ranking strategy.

[0100] Judging from the experimental results, the accuracy of Mran is significantly better than the other three solutions. Specifically, the average accuracy of Mran is 0.722, while the accuracy of the other three solutions are 0.641, 0.613, and 0.714, respectively, which are improvements of 8.1, 10.9, and 1 percentage points. Without using a context-aware vector representation method, we cannot refer to the effective information of other related attributes in the sorting. Without using an encoding mechanism that sorts in chronological order, we cannot simply rely on existing embedding models to obtain a good chronological order. Without using an adaptive interval sorting strategy, the semantics of the time span cannot be reflected in the sorting results.

[0101] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0102] 4 , a timing sequencing device provided by an embodiment of the present application is shown, which specifically includes the following modules:

[0103] The acquisition module 410 is used to acquire a data set, wherein the data set includes data and attribute values ​​corresponding thereto; wherein the data set includes at least two sets of data;

[0104] A time coding module 420 is configured to determine an associated data attribute value of the target data based on the data set, and determine a time code of the target data based on the associated data attribute value;

[0105] The timeliness sorting module 430 is configured to sort the data in the data set according to the time code.

[0106] In one embodiment of the present invention, the time encoding module 420 includes:

[0107] Pre-data and post-data submodules, configured to determine pre-data of target data, post-data of target data, and target data based on the data set;

[0108] An associated data attribute value submodule, configured to determine an associated data attribute value of the target data based on the preceding data of the target data, the following data of the target data, the target data, and the data set;

[0109] An attribute vector submodule, configured to determine an attribute vector of each target data according to the attribute value of the associated data;

[0110] The time coding submodule is configured to determine the time coding of each target data according to the attribute vector of each target data.

[0111] In one embodiment of the present invention, the attribute vector submodule includes:

[0112] An associated data sequence submodule, configured to determine an associated data sequence according to the associated data attribute value;

[0113] The attribute vector submodule of each target data is used to determine the attribute vector of each target data according to the associated data sequence.

[0114] In one embodiment of the present invention, the time coding submodule includes:

[0115] A numerical coding submodule, configured to determine a numerical code according to the attribute vector of each target data;

[0116] An attribute coding submodule, configured to determine an attribute coding according to the attribute vector of each target data;

[0117] The time coding submodule for each target data is used to determine the time coding of each target data according to the attribute coding and the numerical coding.

[0118] In one embodiment of the present invention, the time encoding submodule of each target data includes:

[0119] An adaptive interval submodule is used to obtain the time code of each target data with an adaptive interval through a loss function based on the attribute code and the numerical code; wherein the adaptive interval refers to the distance between the code of each target data attribute value arranged in time sequence and the code of the preset data attribute value.

[0120] In one embodiment of the present invention, the timeliness ranking module 430 includes:

[0121] The timeliness sorting submodule is used to sort the data in the data set in chronological order according to the time code.

[0122] It should be noted that for the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0123] There are some overlapping operation steps in this specific embodiment and the above specific embodiment, so this specific embodiment is only briefly described. For other solutions, please refer to the description of the above specific embodiment.

[0124] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0125] 5 , a computer device for a time sequence sorting method of the present application is shown, which may specifically include the following:

[0126] The computer device 12 is a general-purpose computing device. The components of the computer device 12 may include but are not limited to: one or more processors or processing units 16, memory 28, and a bus 18 connecting different system components (including memory 28 and processing unit 16).

[0127] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, an Audio Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0128] Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0129] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory 30 and / or cache memory 32. The computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write to non-removable, non-volatile magnetic media (commonly referred to as a "hard drive"). Although not shown in Figure 5, a magnetic disk drive for reading and writing to a removable non-volatile magnetic disk (such as a "floppy disk"), as well as an optical disk drive for reading and writing to a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 42 that are configured to perform the functions of the various embodiments of the present application.

[0130] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in a memory. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules 42, and program data, each of which, or some combination thereof, may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0131] The computer device 12 may also communicate with one or more external devices 14 (e.g., a keyboard, pointing device, display 24, camera, etc.), one or more devices that enable an operator to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., a network card, modem, etc.). Such communication may occur via an I / O interface 22. Furthermore, the computer device 12 may also communicate with one or more networks (e.g., a local area network (LAN)), a wide area network (WAN), and / or a public network (e.g., the Internet) via a network adapter 20. As shown in FIG5 , the network adapter 20 communicates with the other modules of the computer device 12 via a bus 18. It should be understood that, although not shown in FIG5 , other hardware and / or software modules may be used in conjunction with the computer device 12, including, but not limited to, microcode, device drivers, redundant processing units 16, external disk drive arrays, RAID systems, tape drives, and data backup storage systems 34.

[0132] The processing unit 16 executes various functional applications and data processing by running the programs stored in the memory 28, such as implementing a timing sorting method provided in an embodiment of the present application.

[0133] That is, when the processing unit 16 executes the program, it implements the following: obtaining a data set, the data set comprising data and corresponding attribute values; wherein the data set comprises at least two groups of data; determining the associated data attribute values ​​of the target data based on the data set, and determining the time code of the target data based on the associated data attribute values; and sorting the data in the data set by timeliness based on the time code.

[0134] In an embodiment of the present application, the present application further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, a timing sorting method as provided in all embodiments of the present application is implemented.

[0135] That is, when the program is executed by the processor, it is implemented as follows: obtaining a data set, wherein the data set includes data and attribute values ​​corresponding thereto; wherein the data set includes at least two groups of data; determining the associated data attribute values ​​of the target data based on the data set, and determining the time code of the target data based on the associated data attribute values; and sorting the data in the data set by timeliness based on the time code.

[0136] Any combination of one or more computer-readable media may be employed. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0137] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0138] The computer program code for performing the operations of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the operator's computer, partially on the operator's computer, as a separate software package, partially on the operator's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the operator's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet). The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referenced to each other.

[0139] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0140] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0141] The above is a detailed introduction to a timing sorting method and device provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.< / val> < / val> < / val>

Claims

1. A time series sorting method, the method is used to sort data according to timeliness, characterized in that: include: Acquire a data set, the data set comprising data and attribute values ​​corresponding thereto one by one; wherein the data set comprises at least two groups of data; Determining an associated data attribute value of the target data according to the data set, and determining a time code of the target data according to the associated data attribute value; The data in the data set are sorted according to timeliness according to the time code.

2. The time sequence sorting method according to claim 1, characterized in that: The step of determining the associated data attribute value of the target data based on the data set includes: Determine the pre-data of the target data, the post-data of the target data, and the target data according to the data set; The associated data attribute value of the target data is determined according to the preceding data of the target data, the following data of the target data, the target data and the data set.

3. The time sequence sorting method according to claim 1, characterized in that: The step of determining the time coding of the target data according to the associated data attribute value comprises: Determine an attribute vector of each target data according to the attribute value of the associated data; The time code of each target data is determined according to the attribute vector of each target data.

4. The time sequence sorting method according to claim 3, characterized in that: The step of determining the attribute vector of each target data according to the attribute value of the associated data comprises: Determining a sequence of associated data according to the associated data attribute value; An attribute vector of each target data is determined according to the associated data sequence.

5. The time sequence sorting method according to claim 3, characterized in that: The step of determining the time encoding of each target data according to the attribute vector of each target data comprises: Determining a numerical code according to the attribute vector of each target data; Determining an attribute code according to the attribute vector of each target data; The time code of each target data is determined according to the attribute code and the value code.

6. The time sequence sorting method according to claim 5, characterized in that: The step of determining the time code of each target data according to the attribute code and the value code comprises: A time code of each target data with an adaptive interval is obtained through a loss function according to the attribute code and the value code.

7. The time sequence sorting method according to claim 1, characterized in that: The step of sorting the data in the data set according to the time code in terms of timeliness comprises: The data in the data set are sorted in chronological order according to the time code.

8. A time sequence sorting device, the device is used to sort data according to timeliness, characterized in that: include: An acquisition module, used to acquire a data set, wherein the data set includes data and attribute values ​​corresponding thereto one by one; wherein the data set includes at least two groups of data; A time coding module, used to determine the associated data attribute value of the target data according to the data set, and determine the time coding of the target data according to the associated data attribute value; The timeliness sorting module is used to sort the data in the data set according to the time code.

9. A computer electronic device, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the steps of the timing sorting method according to any one of claims 1 to 7 when executed by the processor.

10. A computer-readable storage medium storing a computer program, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the timing sorting method according to any one of claims 1 to 7 are implemented.

Images