METHOD AND SYSTEM FOR SELECTING THINGS BASED ON QUALITY OF SERVICE IN WEB OF THINGS

• US 20160277532A1
• Filed: 11/10/2015
• Published: 09/22/2016
• Est. Priority Date: 03/20/2015
• Status: Abandoned Application

• Current Assignee: Electronics and Telecommunications Research Institute
• Original Assignee: Electronics and Telecommunications Research Institute
• Inventors: LEE, Yong Joon, KIM, Mal Hee
• Application Number:
  US14/936,992
• Publication Number:
  US 20160277532A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 5/025 Extracting rules from data
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/61 taking into account QoS or priority requirements
• CPC Class Codes: H04W 4/70 Services for machine-to-machine communication [M2M] or machine type communication [MTC]

Abstract

A method and system for recommending a thing based on quality of service in a web of things environment, the method including (a) obtaining web log data and metadata of at least one thing; (b) computing QoS (Quality of Service) features of the thing from the web log data and metadata; (c) in response to obtaining user'"'"'s review information on the thing, computing QoS grade information on the thing from the review information; and (d) generating a rule for predicting a QoS grade for a thing for which there is no user'"'"'s review information with reference to the QoS grade information on the thing and the QoS features of the thing.

Claims

1. A method for recommending a predetermined thing to a user in a web of things environment, the method comprising:

(a) obtaining web log data and metadata of at least one thing;

(b) computing QoS (Quality of Service) features of the thing from the web log data and metadata;

(c) in response to obtaining user'"'"'s review information on the thing, computing QoS grade information on the thing from the review information; and

(d) generating a rule for predicting a QoS grade for a thing for which there is no review information with reference to the QoS grade information on the thing and the QoS features of the thing.

2. The method according to claim 1,

further comprising:

(e) in response to receiving a request to provide a web of things service from a user, searching a list of things, and computing QoS features for each of the things; and

(f) applying the QoS features of each of the things to the rule to predict a QoS grade for each of the things, and recommending a thing having a high QoS grade to the user.

3. The method according to claim 1,

wherein the QoS features is a collection of data regarding i) features indicating a response time to a request for a web of things service, ii) features indicating a reliability of an extent of normal response to the request for a web of things service for a predetermined period of time, iii) features indicating availability of a thing, and iv) features representing authentication information on the thing.

4. The method according to claim 3,

wherein the rule is a decision tree generated based on machine learning having QoS grade information on the thing and QoS features on the thing as training data.

5. The method according to claim 4,

wherein the decision tree comprises a root node, intermediate node, and leaf node, and

the root node and intermediate node are features of the QoS features, and the leaf node is QoS recommendation grade.

6. The method according to claim 4,

further comprising testing an accuracy of the decision tree.

7. The method according to claim 6,

wherein the testing an accuracy of the decision tree involves collecting QoS features and QoS grade information not used as training data as test data, and

comparing whether or not QoS grade information obtained by applying the QoS features to the decision tree is the same as QoS grade information collected as the test data.

8. The method according to claim 7,

wherein, in response to the decision tree being determined as being inaccurate, updating a rule forming the decision tree.

9. The method according to claim 1,

wherein the QoS grade is an average value of review information obtained from a plurality of users regarding the thing.

10. A system for recommending proper things to a user in a web of things environment, the system comprising:

a QoS features generator configured to obtain web log data and metadata of at least one thing, and to compute QoS features of the thing from the web log data and metadata;

a QoS classifier generator configured to generate a QoS grade classifier for obtaining user'"'"'s review information on the thing, for computing QoS grade information on the thing from the review information, and for predicting a QoS grade for a thing for which there is no review information with reference to the QoS grade information on the thing and the QoS features of the thing; and

a thing recommender configured to, in response to receiving a request to provide a web of things service from a user, search a list of things and compute QoS features for each of the things, input the QoS features of each of the things to the rule to predict a QoS grade for each of the things, and recommend a thing having a high QoS grade to the user.

11. The system according to claim 10,

wherein the web log data is a collection of data regarding at least one of an IP address that requested a web of things service, name of user who requested the web of things service, method name of HTTP request, URI of request resource, amount of data transmitted during the request and response for the web of things service, and features of the response time to the web of things service request time.

12. The system according to claim 11,

wherein the QoS features generator obtains request response and request data from the web log data using a MapReduce function, and computes the QoS features from the resource response and request data.