KR101719926B1 - Transform system and method for heterogeneous data through data collection api - Google Patents

Abstract

The present invention relates to a system and a method for reciprocally transforming heterogeneous data stored through data collection API. A data mapping and matching system related to an embodiment of the present invention includes: a first data collecting API for collecting first learning activity data performed in a first learning environment according to a first data profile; a first repository for storing the first learning activity data collected by the first data collecting API and first meta data which is meta data of the first learning activity data; and a data mapping and matching instance for converting the first learning activity data into learning activity data according to a second data profile different from the first data profile. The data mapping and matching instance includes a structural and syntactic mapping instance table obtained by matching a class and an attribute of a learning activity data according to the first data profile with a class and an attribute of a learning activity data according to the second data profile, and a semantic instance table obtained on the basis of an ontology rule by matching a meaning of the learning activity data according to the first data profile with a meaning of the learning activity data according to the second data profile, comprehends the data profile of the first leaning activity data from the first meta data, converts the class and the attribute of the first learning activity data into the class and the attribute according to the second data profile by using the structural and syntactic mapping instance table, and converts the meaning of the first learning activity data into the meaning according to the second profile based on the ontology rule by using the semantic instance table.

Description

TECHNICAL FIELD [0001] The present invention relates to a system and method for converting between heterogeneous data stored through a data acquisition API,

The present invention relates to a heterogeneous data-to-data conversion system and method collected through a data collection API. More particularly, the present invention relates to a system and method for transforming data between data generated in various platforms and software for learning analysis, structurally and semantically And more particularly, to a system and method for converting data.

A terminal such as a personal computer, a notebook computer, a mobile phone, or the like can be configured to perform various functions. Examples of such various functions include various application driving functions, a web browsing function, data and voice communication functions, a function of shooting a picture or a video via a camera, a voice storage function, a function of playing music and audio files through a speaker system, And a display function of the display device. Some terminals include additional functions to execute games, and some other terminals are also implemented as multimedia devices. Moreover, recent terminals can receive a broadcast or multicast signal and can watch video or television programs. All of these functions can also be used as functions for learning in the field of education.

On the other hand, as smartphones and social network services (SNS) become common, the types and amounts of data generated by billions of users around the world are increasing at an explosive rate, and the awake leaders of government, We are paying attention to data utilization. The government is also cooperating with the global trend by introducing public data as a government 3.0 policy and fostering data-based new industries as a new government operation paradigm.

In particular, Big Data is more like a picture that expresses the needs and behaviors of individuals rather than data representing the group, so it is expected to be very useful in all fields that are oriented toward personalized personalized services. In the field of education, we started to use learning analytics technology to collect and analyze learning-related data, intentionally or unintentionally, as an application of Big Data.

However, it is a reality that systematic and accurate data collection is difficult because user 's learning environment for collecting data, that is, platform and software, is very diverse. To address this problem, some standardization bodies have standardized data profiling and data collection APIs for data collection to provide a systematic data collection system.

On the other hand, since standardized data collection systems are composed of heterogeneous structures and APIs, in some situations one institution or service organization may face the situation of using heterogeneous data collection standards.

In this specification, we propose rules, procedures and methods to ensure interoperability of heterogeneous data collection system and data collected by API.

Korean Patent Publication No. 10-2011-0122267

It is an object of the present invention to provide a heterogeneous data collection system and interoperability between learning data collected and stored from an API.

Specifically, the present invention defines a structured and syntactic mapping method through a systematic identification step and procedure for a data profile of a heterogeneous data collection system, and also uses the semantic mapping between collected data to extract homogeneous and similar semantic data The goal is to provide data interoperability so that the system can recognize it.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention, unless further departing from the spirit and scope of the invention as defined by the appended claims. It will be possible.

According to an embodiment of the present invention, there is provided a data mapping and matching system including a first data collection API for collecting first learning activity data performed in a first learning environment according to a first data profile; A first storage for storing first learning activity data collected by the first data collection API and first metadata that is metadata of the first learning activity data; And a data mapping and matching instance for transforming the first learning activity data into learning activity data according to a second data profile different from the first data profile, The structure and the syntax of the learning activity data corresponding to the class and the attribute of the learning activity data according to the second data profile are correlated with the class and the attribute of the learning activity data according to the second data profile, Wherein the meaning of the learning activity data according to the data profile includes a semantic instance table corresponding to the ontology rule and grasps the data profile of the first learning activity data from the first metadata, Using the table, 1 learning activity data into classes and attributes according to the second data profile and using the semantic instance table to convert the meaning of the first learning activity data into the second data profile according to the ontology rule Can be converted into a meaning according to the following expression.

In addition, each class and attribute of the structured and syntactic mapping instance table may be given a unique identification number.

The data mapping and matching instance may also be configured to add the non-inclusion class to the structural and syntactic mapping instance table if the class of the first learning activity data is not included in the structural and syntactic mapping instance table .

In addition, the data mapping and matching instance may add the non-inclusion attribute to the structural and syntactic mapping instance table if the attribute of the first learning activity data is not included in the structural and syntactic mapping instance table .

The first metadata may also include at least one of a name of a data profile of the first learning activity data stored in the first repository, a standard name and version of the data profile, and a location of the first repository.

In addition, the data mapping and matching instance may be configured such that if the class or attribute of the first learning activity data does not correspond according to the mapping instance table or if the meaning of the first learning activity data does not correspond according to the semantic instance table , An exception process can be performed on the first learning activity data.

And a second storage for storing second learning activity data collected in accordance with the second data profile, wherein the data mapping and matching instance includes a second data profile, 1 learning activity data to the second repository.

In addition, the second storage may perform a conformity check to determine whether the received first learning activity data has the class, attribute, and meaning according to the second data profile, and if appropriate, The first learning activity data may be stored, and if not, exception processing may be performed.

Meanwhile, a data mapping and matching method related to an example of the present invention for realizing the above-mentioned problems collects learning activity data using a first data collection API, a first storage, and a data mapping and matching instance and performs structural and syntactic mapping The first data collection API collecting first learning activity data performed in a first learning environment according to a first data profile; A first step of storing the first learning activity data and the first metadata which are metadata of the first learning activity data; A third step of the data mapping and matching instance querying the first meta data of the first learning activity data stored in the first storage to grasp the data profile of the first learning activity data; A fourth step of the data mapping and matching instance receiving the first learning activity data from the first storage; A fifth step of the data mapping and matching instance converting the classes and attributes of the first learning activity data into classes and attributes according to a second data profile different from the first data profile; And a sixth step of the data mapping and matching instance converting the meaning of the first learning activity data into a meaning according to the second data profile, The structure and the syntax of the learning activity data corresponding to the class and the attribute of the learning activity data according to the second data profile are correlated with the class and the attribute of the learning activity data according to the second data profile, The meaning of the learning activity data according to the data profile includes a semantic instance table corresponding to the ontology rule, and in the fifth step, the data mapping and matching instance are transformed using the structural and syntactic mapping instance table , In the sixth step, the data mapping and / Ching instance may be converted in accordance with said ontology rule using the semantic instance table.

In addition, each class and attribute of the structured and syntactic mapping instance table may be given a unique identification number.

In addition, if the class of the first learning activity data received by the data mapping and matching instance is not included in the structural and syntactic mapping instance table after the fourth step, the data mapping and matching instance is not included A class may be added to the structured and syntactically mapped instance table.

If the attribute of the first learning activity data received by the data mapping and matching instance is not included in the structural and syntactic mapping instance table after the fourth step, the data mapping and matching instance is not included Attribute to the structured and syntactic mapping instance table.

The first metadata may also include at least one of a name of a data profile of the first learning activity data stored in the first repository, a standard name and version of the data profile, and a location of the first repository.

In the fifth step, the class or attribute of the first learning activity data does not correspond to the mapping instance table, or in the sixth step, the meaning of the first learning activity data corresponds to the semantic instance table If not, after the sixth step, the data mapping and matching instance may perform exception processing for the first learning activity data.

In addition, after the sixth step, the data mapping and matching instance may store the first learning activity data converted into learning activity data according to the second data profile as second learning activity data collected according to the second data profile To the second storage for storing the second storage.

In addition, after the seventh step, a suitability test is performed to determine whether the transformed first learning activity data received by the second repository has class, attribute, and meaning according to the second data profile, Storing the transformed first learning activity data, and if not, performing exception processing.

The present invention is to provide interoperability between heterogeneous data utilized for accurate learning analysis by systematically collecting data generated by learning activities.

Specifically, the process of mapping the structure and syntax of heterogeneous data, and the semantic matching process for comparing and analyzing the meaning of each data, finds out agreement or meaningful parts of the data and transforms and exchanges the data. .

It should be understood, however, that the effects obtained by the present invention are not limited to the above-mentioned effects, and other effects not mentioned may be clearly understood by those skilled in the art to which the present invention belongs It will be possible.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate preferred embodiments of the invention and, together with the description, serve to further the understanding of the technical idea of the invention, It should not be construed as limited.
Figure 1 conceptually illustrates a binding structure that is universally followed by heterogeneous data.
Figure 2 illustrates one embodiment of a heterogeneous data exchange system that may be implemented in accordance with the present invention.
FIG. 3 shows a flow of operation of a conversion system through learning data mapping and matching according to an embodiment of the present invention.
Figure 4 shows that heterogeneous data profiles are mapped structurally and syntactically according to one embodiment of the present invention.
FIG. 5A conceptually illustrates a two-step data mapping and matching procedure when there are two data profiles for collecting data in the education field according to an embodiment of the present invention.
FIG. 5B illustrates a concept of transforming data when an ontology mapping instance manages entities and attributes of a data profile according to a predetermined rule when there are a plurality of data profiles for collecting data in the education field according to an embodiment of the present invention .

First, data used in the system of the present invention is generally represented by a triple binding structure as shown in FIG.

Fig. 1 conceptually shows a binding structure of data used in the system of the present invention.

For example, as shown in Fig. 1, in addition to the direct data " who, what, what ", the bound data includes " what kind of tool was used in what situation, what data was generated by the user, Context information about how many points were acquired " is also bound together.

Hereinafter, a preferred embodiment of the present invention will be described with reference to the drawings. In addition, the embodiment described below does not unduly limit the contents of the present invention described in the claims, and the entire configuration described in this embodiment is not necessarily essential as the solution means of the present invention.

Hereinafter, a learning data collection and conversion system to be proposed by the present invention will be described in detail with reference to the drawings. Figure 2 illustrates one embodiment of a process and system for generating, transmitting, and storing learning data that may be implemented in accordance with the present invention.

2, the transformation system 100 with learning data mapping and matching according to the present invention includes a learning environment 10, a data profile 20, a data collection API 30, a data storage 40, (50), and a data mapping and matching process (60).

However, the components shown in FIG. 2 are not essential, so that a data mapping and matching system 100 having more or fewer components may be implemented.

Also, the components shown in FIG. 2 are connected to each other in an interdependent manner, and the components may be implemented separately or integrally as shown in FIG.

Hereinafter, the components will be described in order.

The learning environment 10 can be composed of various environments 11 and 12. The user can participate in learning activities and generate learning data using contents, services, web links, and software provided in the learning environment 10 do.

This learning data is generated along the standardized data profile 20. Examples of data profiles 20 include IMS Caliper Metric Profile 21, xAPIs Recipes 22, and the like. Such a data profile 20 can be utilized variously by education institution, region, and country.

The data generated in accordance with the data profile 20 are acquired, stored and transmitted by the data collection API 30 and these data collection APIs 30 are also stored in heterogeneous forms 31 and 32, . ≪ / RTI > That is, the data generated according to the IMS Caliper Metric Profile 21 is operated by the IMS Caliper Sensor APIs 31, and the data generated according to the xAPIs Recipes 22 can be operated by the xAPIs 32.

The data sent by the data collection API 30 is stored in the data collection store 40 where it is necessary to exchange data between the stores 41 and 42 which store the heterogeneous data, The converted data can be provided by the conversion request. The data mapping and matching instance 60 queries the metadata 51 and 52 to obtain a profile of the data profile and location etc. applied to the requested stores and transforms the data according to the transformation rules for the data profile And then returns to the data store 60 to perform data interoperability.

A representative example of the system is a learning analysis service. In a learning analysis service, a service provider generally provides a learning environment (10) that operates on various devices, and a data collection system provided by standardization organizations such as IMS Global Learning Consortium and Advanced Distributed Learning, IMS Caliper (21) or xAPI 22 to generate and store data using partial or fully compliant data collection APIs 31, 32.

Data collected may include session information, quizzes and test results, reading activities, task performance, media utilization, etc. These data are stored in learning data stores 41 and 42, And transmitted to the user.

Meanwhile, the data profile 20 uses different data profiles and vocabularies, and in particular, the xAPI standard recommends that individual profiles be created and used individually under the name of recipes (xAPIs Recipes) Therefore, heterogeneous data is being generated according to the data collection API.

FIG. 3 shows a flow of operation of a conversion system through learning data mapping and matching according to an embodiment of the present invention.

First, we explain the procedure before heterogeneous data exchange is performed.

Structural and syntactic information of the data profile to be converted is registered in the data mapping and matching instance 60 (S110).

That is, it corresponds to an identification system process that assigns an identification number such as a URI to a class and an attribute of a data profile.

Next, structural and syntactic mapping between heterogeneous data profiles is performed (S120).

This is the process of doing structural and syntactic mapping for data transformation in the following steps.

FIG. 4 illustrates that a heterogeneous data profile is mapped structurally and syntactically according to an embodiment of the present invention. As illustrated in FIG. 4, classes and attributes used in each data profile include a unique identification value as a URI And have an identification scheme that can be mapped to an N: M relationship.

5A is a conceptual representation of a data mapping and matching sequence in two steps when there are two data profiles for collecting data in the education field according to an embodiment of the present invention. As described above, this relationship mapping maps the identification value 111 assigned to the class to the identification value 112 for class-specific attributes into a hierarchical structure.

Next, semantic matching is performed (S130).

That is, in the following steps, the meanings of vocabulary and sentences used in each data profile are analyzed for data conversion, and the agreement and significance are matched.

FIG. 5B illustrates a concept of converting data while managing classes and attributes of a data profile according to predetermined rules when an ontology mapping instance has a plurality of data profiles for collecting data in the education field according to an embodiment of the present invention. As illustrated in FIGS. 5A and 5B, for the semantic mapping, an instance table 140 to which a mapping rule for vocabularies used in each data profile is applied is used.

Next, a procedure for collecting and storing data will be described.

First, the data collection API 30 is informed of the occurrence of the learning activity (S210).

When the user participates in the learning, the learning environment (10) is used. When the learning activity is performed in the learning environment 10, the data collection API 30 receives notification that the data is generated in a synchronous or asynchronous manner.

Next, the data collection API 30 recognizing that the data is generated binds the created data according to the data profile and stores the bound data in an API temporary storage module (S220).

That is, the generated data is temporarily stored according to the data profile.

Next, the data collection API 30 requests authentication in order to transmit the dumped data to the data repository 40 (S310).

This is a procedure for mutual authentication between the data collection API 30 and the data repository 40. The data repository 40 can approve the data collection API 30 when it is authenticated.

Thereafter, the data temporarily stored by the data collection API 30 is transmitted to the data storage 40 (S320).

Next, the data store 40 may perform a conformance test to test whether the received data is created according to the data profile (S330). However, the step S330 may be selectively performed.

Hereinafter, the process of converting data stored in the data repository will be described.

When mutual data exchange is required in the storage 40 in which heterogeneous data is stored, data is transferred from one storage 40 to another storage 40 or from one data store 40 to another storage 40 When collecting data collectively, the data store 40 first makes a data conversion request to the data mapping and matching instance 60 (S410).

Next, the data mapping and matching instance 60 inquires the profile metadata of the data repository in order to grasp the data profile, transmission / reception position information, and the like applied to each data store prior to data reception (S420).

That is, it inquires which data profile stored in the data store is generated according to which data profile, and where the data is stored.

Next, the data store 40 transmits data to the data mapping and matching instance 60 requesting conversion (S430).

Next, the data mapping and matching instance 60 converts the received data into classes and attributes to be converted (S510).

The data mapping and matching instance 60 converts the class and attribute of the received data into the classes and attributes to be converted by using the structured and syntactic mapping instance table.

Thereafter, the data mapping and matching instance 60 matches the meaning of the received data (S520).

The data mapping and matching instance 60 extracts the vocabulary and sentences used in the received data and the converted data and matches the same meaning or the similar meaning using the ontology rule of the instance table.

The data mapping and matching instance 60 may exceptionally process the received data if the mapping at step S510 or the matching at step S520 is not successful.

Next, the data mapping and matching instance 60 transmits the converted data to the data store 40 to be transmitted (S440).

Thereafter, the data storage 40 checks the suitability of the received data, and stores the unsuitable data by exception processing (S450).

The data store 40 optionally performs a conformance check on the received transformed data and, if it is not correctly mapped or semantically matched, stores the exception data in an exceptional manner.

On the other hand, when performing the structural, syntactic mapping (S510) and semantic matching (S520) processes by transferring data from the data store 40 to the data mapping and matching instance 60, new structures, attributes, and vocabularies are received The data mapping and matching instance 60 may update the semantic filter and mapper 113 illustrated in Figure 5B using the ontology rules.

The ontology rules can be updated in an automated way using the structure and syntax of the initially constructed data and the newly received data, and vocabulary placement.

The present invention can also be embodied as computer-readable codes on a computer-readable recording medium. A computer-readable recording medium includes all kinds of recording apparatuses in which data that can be read by a computer system is stored. Examples of the computer-readable recording medium include a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like, and may be implemented in the form of a carrier wave (for example, transmission over the Internet) . In addition, the computer-readable recording medium may be distributed over network-connected computer systems so that computer readable codes can be stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing the present invention can be easily inferred by programmers of the technical field to which the present invention belongs.

The above-described learning data mapping and matching system and method are not limited to the configuration and method of the embodiments described above, but the embodiments can be applied to all of the embodiments so that various modifications can be made. Or some of them may be selectively combined.

10: Learning environment
11: Learning environment using IMS Caliper data profile
12: Learning environment when using xAPI data profile
20: Data Profiles
21: IMS Caliper profile
22: xAPI recipe
30: Data Collection API
31: IMS Caliper Sensor APIs
32: xAPIs
40: Data storage
41: Event storage communicating with the IMS Caliper Sensor API
42: Learning record store communicating with xAPI
50: Data Store Profile Metadata
51: IMS Caliper Data Store
52: xAPI Data Store
60: Data mapping and matching instances

Claims (17)

A system for collecting learning activity data and performing structural and syntactic mapping and semantic matching,
A first data collection API for collecting first learning activity data performed in a first learning environment according to a first data profile;
A first storage for storing first learning activity data collected by the first data collection API and first metadata that is metadata of the first learning activity data;
And a data mapping and matching instance for transforming the first learning activity data into learning activity data according to a second data profile different from the first data profile,
The data mapping and matching instance includes:
A structured and syntactic mapping instance table in which classes and attributes of learning activity data according to the first data profile are associated with classes and attributes of learning activity data according to the second data profile and learning activity data according to the first data profile And the meaning of the learning activity data according to the second data profile includes a semantic instance table corresponding to the ontology rule,
Grasping a data profile of the first learning activity data from the first metadata,
Converting the classes and attributes of the first learning activity data into classes and attributes according to the second data profile using the structural and syntactic mapping instance tables,
Converting the meaning of the first learning activity data into a meaning according to the second data profile according to the ontology rule using the semantic instance table,
Wherein each class and attribute of the structured and syntactic mapping instance table is assigned a unique identification number,
The data mapping and matching instance includes:
If the class of the first learning activity data is not included in the structural and syntactic mapping instance table,
Adding the non-included classes to the structural and syntactic mapping instance tables, adding the non-inclusive attributes to the structural and syntactic mapping instance tables,
Wherein the first metadata includes at least one of a name of a data profile of a first learning activity data stored in the first repository, a standard name and version of a data profile, and a location of the first repository,
The data mapping and matching instance includes:
If the class or attribute of the first learning activity data does not correspond according to the mapping instance table or if the meaning of the first learning activity data does not correspond according to the semantic instance table, Processing the data. delete delete delete delete delete The method according to claim 1,
And a second storage for storing second learning activity data collected according to the second data profile,
Wherein the data mapping and matching instance transmits the first learning activity data converted into the learning activity data according to the second data profile to the second storage. 8. The method of claim 7,
Wherein the second storage comprises:
Attribute and meaning according to the second data profile by performing the conformity check to determine whether the received first learning activity data has the class,
Storing the converted first learning activity data, if appropriate,
And if not, exception processing is performed. 1. A method for collecting learning activity data using a first data collection API, a first storage and a data mapping and matching instance to perform structural and syntactic mapping and semantic matching,
A first step of the first data collection API collecting first learning activity data performed in a first learning environment according to a first data profile;
A first step of storing the first learning activity data and the first metadata which are metadata of the first learning activity data;
A third step of the data mapping and matching instance querying the first meta data of the first learning activity data stored in the first storage to grasp the data profile of the first learning activity data;
A fourth step of the data mapping and matching instance receiving the first learning activity data from the first storage;
A fifth step of the data mapping and matching instance converting the classes and attributes of the first learning activity data into classes and attributes according to a second data profile different from the first data profile; And
And a sixth step of the data mapping and matching instance converting the meaning of the first learning activity data into a meaning according to the second data profile,
The data mapping and matching instance includes:
A structured and syntactic mapping instance table in which classes and attributes of learning activity data according to the first data profile are associated with classes and attributes of learning activity data according to the second data profile and learning activity data according to the first data profile And the meaning of the learning activity data according to the second data profile includes a semantic instance table corresponding to the ontology rule,
In the fifth step,
Wherein the data mapping and matching instances are transformed using the structured and syntactic mapping instance tables,
In the sixth step,
Wherein the data mapping and matching instances are transformed according to the ontology rule using the semantic instance table,
Wherein each class and attribute of the structured and syntactic mapping instance table is assigned a unique identification number,
After the fourth step,
If the class of the first learning activity data received by the data mapping and matching instance is not included in the structural and syntactic mapping instance table,
Wherein the data mapping and matching instance adds the non-included class to the structural and syntactic mapping instance table, adds the non-inclusion attribute to the structural and syntactic mapping instance table,
Wherein the first metadata includes at least one of a name of a data profile of a first learning activity data stored in the first repository, a standard name and version of a data profile, and a location of the first repository,
The class or attribute of the first learning activity data does not correspond to the mapping instance table in the fifth step or that the meaning of the first learning activity data does not correspond to the semantic instance table in the sixth step Occation,
And after the sixth step, the data mapping and matching instance performs exception processing for the first learning activity data. delete delete delete delete delete 10. The method of claim 9,
After the sixth step,
The data mapping and matching instance transmits the first learning activity data converted into learning activity data according to the second data profile to a second storage that stores second learning activity data collected according to the second data profile The method of claim 1, further comprising: 16. The method of claim 15,
After the seventh step,
Attribute and meaning in accordance with the second data profile, the first learning activity data received by the second storage,
Storing the converted first learning activity data, if appropriate,
And if it does not, exception handling. ≪ Desc / Clms Page number 22 > delete

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