Bell Atlantic Network Services, Inc.

Verizon Services Corporation

Richard LaPearl

Thomas A Nolting

Karen Dion

Sheila Noonan

Raymond A Gillis

Carol B Snow

Raymond X Leung

Specialized telecom network users who burden the network, such as high-volume users of Internet services, are identified by analysis of network traffic data to identify addresses (e.g. telephone numbers) for callers to destinations associated with the particular call-in service. Destination telephone numbers may be identified from a list of known service providers or from a network traffic study, which identifies destination numbers receiving unique patterns of incoming traffic. Analysis of the records of calls to the destination numbers then identifies numbers for callers who made more than some minimum number of calls during the study or who accumulate more than some threshold amount of total connect time on such calls. The preferred embodiments utilize automated systems to compile and analyze call records from standard messages of a telephone network, such as interoffice signaling messages or automated accounting messages. Data from the records can be translated to identify the calling subscribers, e.g. by name and address or by point of connection to the network.

Internet user finder

A monitoring system captures and processes messages from SS7 links, to compile call detail records (CDRs) for all interoffice call attempts. The CDRs are uploaded into a relational database. Automatic Message Accounting (AMA) records also are accumulated and uploaded to a another relational database. A data preparation operation utilizes external information to enhance the records for further processing, for example, to translate information codes in the records to textual names of originating and terminating offices. The enhanced records are loaded into a multi-dimensional database. The multi-dimensional database software facilitates analysis of the records to identify a variety of network traffic patterns. The user can automatically set up and customize studies for particular applications, for example to study traffic patterns for calls through a particular office. The multi-dimensional database also enables the presentation of information from the records in formats illustrating a variety of particular features of the traffic found in a study. For example, a report may show minutes of use for calls through an office during different hours of the day or show the other offices communicating with the office under study and the numbers of calls from each of those other offices.

Traffic track usage measurement system

A monitoring system captures and processes messages from SS7 links, to compile call detail records (CDRs) for all interoffice call attempts. The CDRs are uploaded into a relational database. A data preparation operation utilizes external information to enhance the records for further processing, for example, to translate release cause codes in the records to textual indications of reasons for the releases. The enhanced records are loaded into a multi-dimensional database. The multi-dimensional database software facilitates analysis of the records to identify a variety of network traffic patterns and related troubles. In particular, the processing of the enhanced CDR data can provide a variety of analytical tools and reports, using the release cause codes. For example, the analysis can indicate the number of calls to a destination that did not get through and identify by major categories the principle reasons that calls did not go through, e.g. user busy, no trunk available, etc. The analysis also can identify the element that generated the cause of the release code. For example, if there were no trunk available through a particular tandem, as indicated by the release code, the CDRs and analytical report would identify the tandem.

Traffic track measurements for analysis of network troubles

A monitoring system captures and processes messages from SS7 links, to compile call detail records (CDRs) for all interoffice call attempts. The CDRs are uploaded into a relational database. Automatic Message Accounting (AMA) records also are accumulated and uploaded to another relational database. A data preparation operation enhances the records for further processing. This data preparation operation involves translating information in the records into more useful forms, using external reference data regarding the monitored network. For example, the data preparation translates SS7 point codes or NPA-NXX codes in the records to textual names of originating and terminating offices. The data preparation also spreads the usage information from the records to properly allocate usage time to predefined intervals. Another function of the data preparation stage is to form one or more predefined tables from the processed records, for example a table of modified records and one or more specialized summary tables. The data, so prepared, is uploaded to an on-line analytical processing application.

Data preparation for traffic track usage measurement

Specialized telecom network users who burden the network, such as Internet Service Providers (ISPs), are identified by analysis of network traffic data to identify addresses (e.g. telephone numbers) for destinations receiving unique patterns of incoming traffic. For an ISP, in particular, the analysis involves identifying telephone, numbers of destinations receiving a high volume of calls and having connections exhibiting a long average hold time. To further enhance the ISP finder analysis, the call data may be examined to confirm that there is no outgoing call traffic from any station associated with the candidate ISP numbers. When numbers are identified by the call data analysis, a technician can call each number and listen for a modem tone, as a confirmation that each candidate number actually is of a data service, such as an ISP. Similar methodologies can identify destination numbers for other unique service providers, such as credit card verification services. The preferred embodiments utilize automated systems to compile and analyze call records from standard messages of a telephone network, such as interoffice signaling messages or automated accounting messages.

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Princeton, MA, United States of America

Richard LaPearl