WO2000036534A2 - Controlling and marketing method for utilisation of the internet/intranet - Google Patents
Controlling and marketing method for utilisation of the internet/intranet Download PDFInfo
- Publication number
- WO2000036534A2 WO2000036534A2 PCT/DE1999/003922 DE9903922W WO0036534A2 WO 2000036534 A2 WO2000036534 A2 WO 2000036534A2 DE 9903922 W DE9903922 W DE 9903922W WO 0036534 A2 WO0036534 A2 WO 0036534A2
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- WO
- WIPO (PCT)
- Prior art keywords
- data
- internet
- intranet
- rule
- behavior
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3438—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment monitoring of user actions
Definitions
- the subject of the registration relates to a method for recording the usage behavior of a participant on the Internet / intranet comprising the features of claim 1.
- Non-contractual use of Internet access e.g. by private individuals at flat rate tariffs of an Internet service provider or by employees of a company
- it is technically very difficult to identify such cases of use so early that it is possible to react in good time.
- Targeted marketing campaigns for the various usage groups on the part of the Internet service provider, recognizing market trends or also determining a company's cost-saving potential (e.g. in corporate networks) have been very difficult so far, since the precise assignment of an Internet / Intranet user to various behavioral categories is not adequately supported.
- the object of the application is based on the problem of specifying a method which has an increase in the significance of the statement with a reduced error rate compared to conventional methods.
- the invention provides Internet service providers and companies with extremely good statements regarding - the type of use of the Internet / intranet (in particular also regarding non-contractual use) - market trends (in particular also about abrupt changes in behavior regarding the use of Internet / intranet) - marketing a necessary network expansion etc.
- this invention eliminates the disadvantages of the data mining tools on the market.
- the results of the individual methods are gnifikanten statement with extremely low error rate compaction ⁇ tet (combined linked).
- FIG. 2 shows a basic block diagram of elements and their interaction in the subject of the application
- FIG. 3 shows an application example for modeling a behavioral category in the causal network.
- the Internet data Idat are correlated and processed in a preprocessing PP (for: preprocessing) according to fixed rules for Internet Data Records (IDR) condenses;
- IDR Internet Data Records
- the Internet data records with rules entered by the operator e.g. in the form of select statements
- RETR for: retrieval, data ining
- the Internet data Idat of a rule-based preliminary RBPP (for: rule-based preprocessor), whereby the Internet data is correlated and compressed.
- the Internet data possibly in preprocessed form, can be subjected to an intermediate storage INTM (for: Interim Memory) as an intermediate result.
- the Internet data possibly in preprocessed form and possibly after buffering, is supplied to a method approach MA which contains a rule-based approach RBA, a neural network with monitored training NNUE, a density-based profile modeling DBPM and a causal neural network KNN, in the following causal network called, has.
- the method approach MA works, as indicated by two double-headed arrows, with a rule base RB, in which the rules are stored, a database MO / TR, in which the modeling / training data are stored, and a database HIST, in which the evaluation results of the current and previous observation periods are stored together.
- the intermediate results output by the method approach MA or stored in the database HIST can be subjected to an evaluation in a device COMB (for: combination) and are output as the result OUT.
- the method according to the application comprises a combination of four different method approaches, the rule-based approach and three method approaches of neuroinformatics (neural network with monitored training, density-based modeling and causal network).
- the rule formulation or the modeling using the methods of neuroinformatics are carried out on the basis of data that the Internet service provider or a company saves: RADIUS accounting data (usually saved), TCP dump protocol data (saved if required, scope variable), SNMP (Simple Network Management Protocol) data (storage if required) etc.
- the resulting model represents a controlling and marketing tool.
- the application method is intended for use on Internet / Intranet data that stores the Internet Service Pro ⁇ vider or a company. Such data include the RADIUS accounting data to TCP dump data and the SNMP data.
- the method can any other Internet / intra ⁇ net data edit.
- the RADIUS accounting data is given by data as described in the IETF specification RFC 2139.
- a real implementation is e.g. in Livingston Enterprises Inc., Radius dictionary, VI.6, 1997.
- the TCP dump data is given by data as described in UNIX man-pages tcpdump - dump traffic on a network '.
- the SNMP data are given by data as described in the various RFCs of the IETF.
- An actual implementation is e.g. in Livingston Enterprises Inc., Configuration SNMP, Manual Portmaster 3.
- a rule-based preprocessor can optionally be used.
- the preprocessor has the task of correlating and compressing the Internet / Intranet data in such a way that data sets are delivered with the attribute values required in the actual process.
- a preprocessor can be used, as is provided in the proposed solution. However, this presupposes that the IDR contains a superset of the attribute values required by the method.
- a rule-based preprocessor is used.
- the rules control the correlation and compression of the Internet / Intranet data. Is added in the actual process, a new attribute or characteristic falls a characteristic AttributeDescriptor ⁇ but off, the selection rules of the preprocessor mono- times may be adjusted (automatically).
- An automatic ANPAS ⁇ solution of the selection rules can, as in Figure 2 with ADAP (for: Adaptation) designated via Notifications (unsolicited messages) can be controlled to the Preprocessor.
- the actual process is divided into four processes. Each method uses a different method approach.
- the four different method approaches are: - the rule-based procedure,
- typical user-specific behavior categories can be modeled using rules.
- Behavior is classified by a behavior category, for example the behavior categories "private use student”, “private use employee”, “private use freelancer”, “use small business”, “use large business”, “player”, “internet / intranet” Addict ",” users with high mail volume "etc. can be expressed by their characteristic properties in the form of rules.
- the rules are applied to all Internet / Intranet data or part of this data (eg the result of preprocessing).
- the result of the method is that after a period of observation t each user does not can be assigned to one or more behavior categories. The observation period can vary depending on the behavior category and the desired purpose of the observation.
- the goal is to formulate rules for each behavior category.
- the rules are described with the help of logical expressions in which the fields (attributes) of the different data records are used as variables, e.g.
- Use of "private contract employee”:: applies to all data records of the observation period: usage time Monday to Friday between 5 p.m. and midnight and usage time on weekends from 0 a.m. to midnight and data transfer rate ⁇ 2 megabytes per usage and max.
- Usage time 2 hours
- the rules can refer to one or more data records (including different files).
- a neural network is trained with a set of examples.
- the prerequisite for the training is that the associated target value is given for each example, i.e. at the time of the training it must be known whether e.g. for the example under consideration, there was or was not a specified use (specified uses can be, for example, "breach of contract by private contract employee", “use of focus on surfing", “use of focus on players” etc.)
- the target values to be examined and the attributes characteristic of the example must be specified.
- the characteristic attributes determine the behavior of a user. The behavior in turn depends on certain attribute values (the data itself). Characteristic attributes can e.g. his:
- the aim is to create a model that, based on the given example, decides for a user whether or not the Internet / Intranet access is used with regard to one or more defined target values.
- the model is created by the supervised training, the basics of which are in Rumelhart, DE, Hinton, GE and Williams, RJ Learning infernal representation by error backpropagation, In Parallel Distributed Processing, pp. 318-362, Cambridge, MA, MIT Press, 1986 ) are described in detail.
- Each user is assigned a behavior pattern in the form of attributes that describes a certain profile over a longer period of time.
- the attributes characterize the use with regard to a defined target value.
- the period on which the behavior pattern is based should not be shorter than four weeks and before the point in time when the method for the above-mentioned Purpose is applied.
- the neural network is trained on the use of the defined target values using training data. With the training data, it is known whether the use can be assigned to a specific target value or not.
- the neural network decides whether the use can be assigned to a specific target value or not. This user-specific decision is logged in the HIST database as the result of the observation period. If necessary, the neural network can be trained with new target values with regard to its use (for example cases of contract violations not yet known).
- This method is applicable when the user is part of the data.
- Density-based profile modeling is a probabilistic modeling of the behavior of each user (probabilistic profile modeling), i.e. a model is created for each user based on the examples belonging to this user. These examples consist of characteristic attributes and certain attribute values that describe the use of the Internet / intranet with regard to one or more target values. Examples of characteristic attributes are described in the previous section.
- the period of time on which the behavior pattern is based should not be shorter than four weeks and before the time when the method is used for control and marketing purposes.
- a probabilistic profile is created for each user. This is done by density estimation using the EM algorithm. The exact description is contained in Chris Bishop, Neural Networks in Pattern Recognition, Oxford Press, 1996.
- the application phase of density-based profile modeling begins, in which the following steps are carried out continuously: The data, for example, one day in terms of for the probabilistic profiling of certain data contents is analyzed (a new example is created).
- the density-based profile model outputs a value that represents a probability of using the internet / intranet of the entity under consideration with regard to the defined target values. This value is logged.
- this value differs from the previous values beyond a predefined threshold value, then there is a message that the result should be displayed in any case.
- This method can be used to easily determine if the use of the Internet / intranet suddenly changes In the current example, the profile model is adapted. This method can be used if the user is part of the data. Powerful of this method: - Detection of an abrupt change in user behavior - Learning ability
- the basis for the method of the causal network is the modeling of typical behavior scenarios in the form of causal dependencies and probabilities of certain data contents, as shown in the example "private use of employees" m Fig. 3.
- a private use of employee PA is assigned a certain usage time UC (for: UseClock), a certain usage period UT (for: UseTi e) and a certain transmission rate RATE.
- the days of the week depending on whether it is a working day WD (for: working day) or a weekend WE (for: week end), influence the amount of time of use, the length of use and the transmission rate.
- the causal dependencies are based on the evaluation of known cases. They do not have to be assigned to specific users.
- the results are logged in a user-specific manner.
- the probabilities behind the causal dependencies can be re-adapted.
- the causal dependencies on new, as yet unknown categories are added to the existing causal dependencies as required.
- This method can also be used when the user is not part of the data set. In this case, a category cannot be assigned to a specific user.
- Strength of the method of the causal network - Assignment of border areas in behavior categories- Recognition of border areas in behavior categories- Learning ability It is basically possible to output the individual results of the individual procedures.
- the individual results of the individual processes are condensed into one overall result. This consolidation includes the individual results of the different processes. The individual results can come from both the current and past observation periods.
- a user x is, for example, clearly assigned to the behavior category "private use freelancer” if he has used the Internet for more than 2 hours in one day (based on the example rule shown above). However, the causal network sees this user x rather in the behavior scenario "private use of employees", since, for example, he has kept the usage time of less than 2 hours in more than 90% of the data records. These findings could then be result to be displayed in such a way that the use of the Internet / intranet by the user x is, with a few minor exceptions, a "private use of employees”.
- Another example of a compression is the trend detection by evaluating the results of different observation periods.
Abstract
Description
Claims
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP99967865A EP1279133A2 (en) | 1998-12-11 | 1999-12-08 | Controlling and marketing method for utilisation of the internet/intranet |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE19857336A DE19857336C1 (en) | 1998-12-11 | 1998-12-11 | Control and marketing process for use of internet/Intranet |
DE19857336.7 | 1998-12-11 |
Publications (2)
Publication Number | Publication Date |
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WO2000036534A2 true WO2000036534A2 (en) | 2000-06-22 |
WO2000036534A3 WO2000036534A3 (en) | 2002-10-24 |
Family
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PCT/DE1999/003922 WO2000036534A2 (en) | 1998-12-11 | 1999-12-08 | Controlling and marketing method for utilisation of the internet/intranet |
Country Status (3)
Country | Link |
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EP (1) | EP1279133A2 (en) |
DE (1) | DE19857336C1 (en) |
WO (1) | WO2000036534A2 (en) |
Families Citing this family (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
ITMI20001952A1 (en) * | 2000-09-05 | 2002-03-05 | Nicola Carena Edgardo Di | METHOD FOR CLASSIFICATION AND TRANSFER OF KNOWLEDGE BETWEEN USERS ACCESSING AN INFORMATION SYSTEM |
DE102008056961A1 (en) * | 2008-11-03 | 2010-05-06 | Aurenz Gmbh | Arrangement for logging and controlling user operations |
US9654590B2 (en) | 2009-06-26 | 2017-05-16 | Telefonaktiebolaget L M Ericsson | Method and arrangement in a communication network |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5197127A (en) * | 1990-09-24 | 1993-03-23 | International Business Machines Corporation | Expert system method for performing window protocol-based data flow analysis within a data communication network |
US5787253A (en) * | 1996-05-28 | 1998-07-28 | The Ag Group | Apparatus and method of analyzing internet activity |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
AU2891697A (en) * | 1996-05-14 | 1997-12-05 | Jorg Arnold | Process and device for charging fees for the use of a telecommunication network |
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1998
- 1998-12-11 DE DE19857336A patent/DE19857336C1/en not_active Expired - Fee Related
-
1999
- 1999-12-08 EP EP99967865A patent/EP1279133A2/en not_active Ceased
- 1999-12-08 WO PCT/DE1999/003922 patent/WO2000036534A2/en active Application Filing
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5197127A (en) * | 1990-09-24 | 1993-03-23 | International Business Machines Corporation | Expert system method for performing window protocol-based data flow analysis within a data communication network |
US5787253A (en) * | 1996-05-28 | 1998-07-28 | The Ag Group | Apparatus and method of analyzing internet activity |
Non-Patent Citations (2)
Title |
---|
LIZCANO P J ET AL: "MEHARI: a system for analysing the use of the internet services" COMPUTER NETWORKS, ELSEVIER SCIENCE PUBLISHERS B.V., AMSTERDAM, NL, Bd. 31, Nr. 21, 10. November 1999 (1999-11-10), Seiten 2293-2307, XP004304653 ISSN: 1389-1286 * |
PITKOW J: "In search of reliable usage data on the WWW" COMPUTER NETWORKS AND ISDN SYSTEMS, NORTH HOLLAND PUBLISHING. AMSTERDAM, NL, Bd. 29, Nr. 8-13, 1. September 1997 (1997-09-01), Seiten 1343-1355, XP004095329 ISSN: 0169-7552 * |
Also Published As
Publication number | Publication date |
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EP1279133A2 (en) | 2003-01-29 |
WO2000036534A3 (en) | 2002-10-24 |
DE19857336C1 (en) | 2000-03-09 |
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