Abstract:
A method for aggregating network flow information within a relational database relates to by maximizing concurrency between insertion and analysis of database records. In particular, data is generally stored according to the network devices associated with the flow records. Then, the flow records for the separate devices may be aggregated at certain time intervals and separately organized. In this way, contention is decreased as analysis can occur on the aggregated flow records, while new flow records are stored. In another embodiment, the aggregated data can be reaggregated again at a second, larger time interval.

Description:
FIELD OF THE INVENTION 
       [0001]    The present invention relates to aggregating network flow information within a relational database by maximizing concurrency between insertion and analysis of database records. In particular, the aggregating of network flow information within a relational data store minimizes the latency of insert and query operation for large sets of data. 
       BACKGROUND OF THE INVENTION 
       [0002]    Network usage data is useful for many important business functions, such as subscriber billing, marketing &amp; customer care, product development, network operations management, network and systems capacity planning, and security. Network usage data does not include the actual information exchanged in a communications session between parties, but rather includes numerous usage detail records, known as “flow records” containing one or more types of metadata (i.e., “data about data”). Known network flow records protocols include Netflow®, sFlow®, jFlow®, cFlow® and Netstream®. As used herein, a flow record is defined as a small unit of measure of unidirectional network usage by a stream of IP packets that share common source and destination parameters during a time interval. 
         [0003]    The types of metadata included within each flow record vary based on the type of service and network involved and, in some cases, based on the particular network device providing the flow records. In general, a flow record provides detailed usage information about a particular event or communications connection between parties, such as the connection start time and stop time, source (or originator) of the data being transported, the destination or receiver of the data, and the amount of data transferred. A flow record summarizes usage information for very short periods of time (from milliseconds to seconds, occasionally minutes). Depending on the type of service and network involved, a flow record may also include information about the transfer protocol, the type of data transferred, the type of service (ToS) provided, etc. In telephony networks, the flow records that make up the usage information are referred to as call detail records (CDRs). 
         [0004]    In network monitoring, the network flow records are collected, stored and analyzed to produce meaningful result. Network usage analysis systems process these flow records and generate reports or summarized data files that support various business functions. Network usage analysis systems provide information about how a network services are being used and by whom. Network usage analysis systems can also be used to identify (or predict) customer satisfaction-related issues, such as those caused by network congestion and network security abuse. In one example, network utilization and performance, as a function of subscriber usage behaviour, may be monitored to track a user&#39;s experience, to forecast future network capacity, or to identify usage behavior indicative of network abuse, fraud and theft. 
         [0005]    As networks become larger and as more tasks are performed within the networks, such as transferring conventional telephone communications to Voice over IP (VOIP), the network flow on the data transactions can be voluminous and will quickly exceed storage and processing capacities. 
         [0006]    In response to this problem of the large volume of the collected network flow information, one known solution uses sampling techniques to decrease data flow volume. Different sampling methods can be used by the network device to collect the information. Sampling can be done at the packet level or the flow level, and can be random or deterministic. Depending on which type of sampling method used, the effect will apply to CPU/memory utilization on the network device and/or bandwidth usage to export flow information to the collector. While the sampling may reduce the overall volume of collected network flow information, the total amount of data is often still voluminous. Furthermore, sampling does not address other problems within current network monitoring methodologies. For example, sampling techniques may not provide a proper picture of the network traffic because some data is being ignored in the process. 
         [0007]    For example, another problem with current network monitoring methodologies is a contention in storage resources when trying to access the stored network flow information as additional network flow information is regularly being added. Typically, as network flow data is being accessed for analysis, new network flow information cannot be stored. Likewise, as new network flow information is in the process of being stored, the existing network flow data typically cannot be accessed. 
       SUMMARY OF THE INVENTION 
       [0008]    In response to these and other needs, embodiments of the present invention relate to a method for aggregating network flow information within a relational database by minimizing the number of database objects required for the aggregation. In particular, flow records are generally stored according to the associated network device. Subsequently, the raw flow records are aggregated into separated tables associated with the certain time intervals and separately organized. In this way, contention is decreased as analysis can occur on the aggregated flow records, while new flow records are stored. 
         [0009]    In one embodiment, a system for aggregating network flow information includes a storage system. The storage system includes a raw data table containing raw flow record data for a current time period for a first network device and a second raw data table containing raw flow record data for a current time period for a second network device. The system further includes a aggregated data table containing aggregated flow record data for the first and the second network devices. Optionally, the storage system further includes a aggregated data table containing aggregated flow record data for the first and second network devices in a first prior time period, and a second aggregated data table containing second aggregated flow record data for a second prior time period. The first period and the second period do not overlap. Optionally, the first period and the second period are of equal duration. Otherwise, the first period has a first duration and the second period has a second, relatively greater duration. For example, the flow records may be aggregated every hour, day, and week. The system of may further include a flow generating device and a data analysis device, whereby the flow generating device is configured to concurrently provide new flow records to the raw data table as the data analysis device accesses the first aggregated data table. The system may also include an archival storage system that is configured to store the raw flow record data for the first network device. 
         [0010]    In another embodiment, a method for aggregating network data flows includes, during a first period of time, storing first flow records in a first table and second flow records in a second table, and after the first period of time and during a second period of time, creating an aggregated table to aggregate both the first and the second flow data records. The method of may also include the steps of, after the second period of time and during a third period of time, creating a third table, storing flow records in the third table, and aggregating flow records from the second period in a second aggregated table. The first period and the second period may be of equal duration, or the first period is relatively longer than the second period. Optionally, the steps of storing new raw flow records and aggregating existing flow records occur concurrently. The method may further include the steps of archiving the first flow records and the second flow records, and compressing the archived first and second flow records. 
         [0011]    In another embodiment, a system for aggregating network flow information includes two or more flow generating devices configured to access a storage system to provide flow records, a storage system configured to store the flow records and to store aggregated flow records corresponding to flow records associated with the network components, wherein, each of the first and second flow records has a time stamp within a first predefined range and each of the aggregated flow records has a time stamp outside of the first predefined range. Also, the system includes a data analysis tool configured to access the storage system, wherein the flow generating device and the data analysis device are configured to access the storage system concurrently. Optionally, the aggregated data table may include sub-tables, wherein each of the sub-tables is associated with a separate time period, and each of the sub-tables includes one or more of the aggregated flow records having a time stamp corresponding to the time periods. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0012]    The above and other objects, features and advantages of certain exemplary embodiments of the present invention will be more apparent from the following detailed description taken in conjunction with the accompanying drawings in which: 
           [0013]      FIG. 1  depicts a flow records analysis system in accordance with embodiments of the present invention; 
           [0014]      FIG. 2  depicts an exemplary flow record in accordance with embodiments of the present invention, and 
           [0015]      FIGS. 3 ,  4 A- 4 B, and  5  depict databases for aggregating the flow records in the flow records analysis system of  FIG. 1  in accordance with embodiments of the present invention 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       [0016]    As shown in  FIG. 1 , a network usage analysis system  100  includes a data collection system server  130  and a data storage system  140 , in one embodiment. The data collection system server  130 , also called a listener, is a central server that collects the flow datagrams  190  from all various network agents  120  to storage and analysis. The data collection system server  130  receives flow records  190  from the flow record generating device  120 , which is a network device that is part of an IP network  110 . In one embodiment, network  110  includes the Internet  115 . 
         [0017]    In general, flow record generating devices  120  may include substantially any network device capable of handling raw network traffic at “line speeds” and generating flow records from that traffic. Exemplary flow record generating device  120  include routers, switches and gateways, and in some cases, may include application servers, systems, and network probes. In most cases, the small flow record records generated by flow record generating devices  120  are exported as a stream of flow records  190  to the data collection system server  130 . 
         [0018]    Various network protocol run on network equipment for collecting network and internet protocol traffic information. Typically, various network agents  120 , such as routers, have flow feature enabled to generate flow records. The flow records  190  are typically exported from the network agent  120  in User Datagram Protocol (UDP) or Stream Control Transmission Protocol (SCTP) packets and collected using a flow collector. For more information, please refer to Internet Engineering Task Force (IETF) standard for Internet Protocol Flow Information eXport (IPFIX) at http://www.ietf.org/html.charters/ipfix-charter.html. 
         [0019]    As described above, flow records  190  are usually sent by the network agents  120  via a UDP or SCTP, and for efficiency reasons, the network agents  120  does not store flow records once they are exported. With a UDP flow, if the flow record  190  is dropped due to network congestion, between the network agent  120  and the data collection server  130 , it may be lost forever because there is no way for the network agent  120  to resend the flow record  190 . Flow may also be enabled on a per-interface basis to avoid unnecessarily burdening of the router&#39;s processor. Thus, the flows records  190  are generally based on the packets input to interfaces where it is enabled to avoid double counting and to save work for the network agent  120 . Also, the network agent  120  may export a flow records for dropped packets. 
         [0020]    Network flows have been defined in many ways. In one implementation, a flow includes a 5-tuple: a unidirectional sequence of packets to define Source IP address, Destination IP address, Source TCP port, Destination TCP port, and IP protocol. Typically, the network agent  120  will output a flow record when it determines that the flow is finished. The network agent  120  does this by “flow aging,” where the network agent  120  resets an aging counter when the network agent  120  sees new traffic for an existing flow. Also, TCP session termination in a TCP flow causes the network agent  120  to expire the flow. The network agent  120  can also be configured to output a flow record at a fixed interval even if the flow is still ongoing. Alternatively, an administrator could define flow properties on the network agent  120 . 
         [0021]    A flow record  190  can contain a wide variety of information about the traffic in a given flow. An exemplary flow record  200  contains the following values, as defined in  FIG. 2 . In particular, a typical flow records  200  may include a version number  210  to identify the type of flow being used. A sequence number  220  identifies the flow record. 
         [0022]    Continuing with  FIG. 2 , input and output interface simple network management protocol (SNMP) indices  230  may be used to dynamically identify network devices through SNMP. SNMP is used by network management systems to monitor network-attached devices for conditions that warrant administrative attention, and consists of a set of standards for network management, including an Application Layer protocol, a database schema, and a set of data objects. SNMP exposes management data in the form of variables on the managed systems, which describe the system configuration. These variables can then be queried (and sometimes set) by managing applications. Modular devices may renumber their SNMP indexes whenever slotted hardware is added or removed. Index values are typically assigned at boot time and remain fixed until the next reboot. 
         [0023]    Continuing with  FIG. 2 , each of the flow records  200  further typically include information on the data transmission, including a time stamps of start and finish times  240 . Other information on the data transmission includes information on the number of bytes and/or packets in a flow  250 . The conditionals of the data transfer may also be included in the flow record  200 , such as header data  260  describing the source and destination addresses, the source and destination addresses port numbers, transmission protocol, and the type of service (ToS). For Transmission Control Protocol (TCP), the flow record  200  may further indicate the union of all TCP flags during the flow. As well known from TCP, a data transmission involves a series of communications confirm, for example, by pairs of acknowledgements flags (ACKs). An imbalance of TCP flags suggests a message failure, whereby a message was sent and never received. 
         [0024]    As suggested above, acquiring and storing the flow data can be computationally expensive for the router and burden the router&#39;s processor to the point where it runs out of capacity. To reduce problems caused by processor exhaustion in the network agent  120 , the above described sampling techniques, may be used in another embodiment of the present invention. When sampled flows are used, the flow records  190  can be adjusted for the effect of sampling, and various values such as traffic volumes are estimations rather than an actual measured flow volume. 
         [0025]    The lack of reliability in the UDP transport mechanism does not significantly affect the accuracy of the measurements obtained from a sampled flow. For example, if flow samples are lost, then new values will be sent when the next polling interval has passed. In this way, the loss of packet flow samples is a slight reduction in the effective sampling rate. When sampling is used, the UDP payload contains the sampled flow datagram. Thus, instead of including an entire flow record  190  each datagram instead provides information such as the flow version, its originating agent&#39;s IP address, a sequence number, how many samples it contains and the flow samples. 
         [0026]    Continuing with  FIG. 1 , the data collection system server  130  receives the streaming flow records  190  from flow record generating device  120  via a communication link  170 . In one embodiment, the flow record generating device  120  may be included within network  110 . In another embodiment, the flow record generating device  120  may be implemented at a location physically apart from, though functionally coupled to, network  110 . Though shown in  FIG. 1  as separate from the data collection system server  130 , flow record generating device  120  may be a part of data analysis system server  130 , in another embodiment. 
         [0027]    A data analysis system server  150  accesses and uses the flow records  190  to perform predetermined network usage statistical analysis. In general, the data analysis system server  150  implements various statistical model that are defined to solve one or more network usage related problems, such as network congestion, network security abuse, fraud and theft, among others. The data analysis system server  150  uses the flow records  190  and the statistical models to generate a statistical result, which also may be subsequently stored within a data storage system  140 . Exemplary embodiments for storing the statistical result will be described in more detail below. By analyzing flow data, the data analysis system server  150  can build a picture of traffic flow and traffic volume in a network. 
         [0028]    In one aspect, the data analysis system server  150  may be responsive to a user interface  160  for interactive analysis of the flow records  190 . User interface  160  may comprise substantially any input/output device known in the art, such as a keyboard, a mouse, a touch pad, a display screen, etc. In one example, a graphical display of the statistical results may be output to a display screen at user interface  160 . 
         [0029]    In one embodiment, data analysis system server  150  comprises a computer software program, which is executable on one or more computers or servers for analyzing the network usage data in accordance with various embodiments of the invention. Although the data storage system  140  is shown as external to the data collection system server  130  and/or the data analysis system server  150 , the data storage system  140  could be alternatively arranged within either of the servers  130  and  150 . Data storage system  140  may comprise substantially any volatile memory (e.g., RAM) and/or non-volatile memory (e.g., a hard disk drive or other persistent storage device) known in the art. 
         [0030]    As previously suggested, while the each of the flow records  190  is typically compact, even small sized networks  100  may have a large number of data transactions, thereby creating a large number of the flow records  190  since there are constant data exchanges within the network  100 . As a consequence, network usage analysis system  100  may produce and store numerous flow records  190  in the data storage system  140  during a given time period. 
         [0031]    Continuing with  FIG. 1 , in response to these and other needs, embodiments of the present invention aggregate the flow records  190  stored in the data storage system  140  depending on the characteristics of the flow records  190 . As described in greater detail below, the aggregation in the data storage system  140  addresses many problems related to the large volume of the flow records  190  and the need to access the existing records  190  while writing new flow records  190 . In the in one embodiment of the present invention, the flow records  190  may also be stored redundantly and entirely in an archival data storage system  199  in which no data aggregation occurs. For example, the flow records  190  may be acquired as needed, even after aggregation in the data storage system  140 . The flow records  190  may be forwarded to the archival data storage system  199  concurrently with the delivery of the flow records  190  to the data storage system  140 . Alternatively, the data storage system  140  may write the flow records  190  to the archival data storage system  199  as part of the aggregation process. Because the archival data storage system  199  is rarely accessed, the flow records  190  stored within it may be significantly compressed using known techniques while substantially preserving all of the data contained within the flow records  190 . 
         [0032]    Referring now to  FIG. 3 , the data storage system  140  for aggregating the numerous stored flow records  190  in accordance with an embodiment of the present invention is now presented. In particular, the data storage system  140  is typically a standard query language (SQL) database  300  on a storage area network (SAN). The database  300  includes multiple tables  310   a - 310   n  and  315  that divide the database  300 . 
         [0033]    In the depicted embodiment, tables  310   a - 310   n  store raw flow records  190   a - 190   n , as described above in  FIG. 2 . In particular, the raw flow records  190   a - 190   n  are flow records that associated, respectively, with different network devices a through n. The flow records  190   a - 190   n  typically identify the device associated with the flow records. For example, in  FIG. 2  above, the exemplary flow record  200  allows identification of a node by either a SNMP index, IP address, or port number. Likewise, tables  310   a - 310   n  are each associated with a different network device a through n. As flow records  190   a - 190   n  are received in the database  300 , the flow records  190   a - 190   n  are stored, respectively, in the tables  310   sa - 310   n . After some trigger event, such as when one of the tables  310   a - 310   n  becomes full, the stored flow records  190   a - 190   n  in tables  310   a - 310   n  are identified, aggregated, and moved to table  315 . 
         [0034]    For example, in one embodiment, data flows records  190   a - 190   n  from the current time period are stored in raw tables  310   a - 310   n . When the flow records  190   a - 190   n  in the current tables  310   a - 310   n  age beyond a predefined threshold, which may be defined according to the storage and access needs of the system  100 , the flow records  190   a - 190   n  are aggregated as desired as the aggregated flow data  301 , and the current raw tables  310   a - 310   n  are reset or a new current tables  310   a - 310   n  are created using the Data Definition Language (DDL) functions to store new flow records  190 . Likewise, the aggregated tables  315  contains, aggregated flow data  301  for the devices a-n. 
         [0035]    In this way, the aggregation generally occurs along one or more of the above-described data categories within the flow record. For example, the aggregated may describe all data of a type or protocol transmitted to or from a particular router a during a time period. 
         [0036]    Continuing with  FIG. 3 , it can be seen that the Flow Record Generating Device  120  may forward new flow records  190  in the current tables  310   a - 310   n . At the same time, the Data Analyzer  150  may access the aggregated data  301  in the aggregated table  315 . In this way, data contention is minimized. Furthermore, because the aggregated data  301  are significantly smaller than the flow records  190   a - 190   n , significant storage capacity is freed. Also, the analysis is greatly eased since the aggregated data  301  are pre-processed and, therefore, significantly smaller. 
         [0037]    As described above, the embodiment depicted in  FIG. 3  generally describes the periodic and cyclical aggregation and storage of the current flow records for each of n network devices. It should be appreciated that the duration of the time periods may be defined as needed to accomplish the goals of the data analysis system  100 . For example, data may be aggregated every few minutes, hourly, or daily. 
         [0038]    The aggregated data  301  in the aggregated table  315  may be formed as needed, according to known aggregation techniques. One record in the aggregated data  301  for a time period may include an aggregated flow describing all communications of a particular type during that time period. In this way, a separate record may by used for communications between the node of interest and different nodes, or different types of communications (different protocols, QoS, etc.) between the node of interest and different nodes. 
         [0039]    Referring now to  FIG. 4A , in another embodiment of the present invention, the data storage system  140  is a database  400 . The database  400  includes a raw tables  410   a  and  410   b  and aggregated tables  415 and  420   a - 420   n that divide the database  400 . In the depicted embodiment of  FIG. 4A , the tables  410   a  and  410   b  are used in the current time period to store each of the new flow records  190   a  and  190   b . While a network  400  having two network devices a and b is presented, it should be appreciated that any number of network devices may be used and accommodated in accordance within the principles of the present disclosure. 
         [0040]    Similar to  FIG. 3 , in the depicted embodiment, of  FIG. 4A , tables  410   a  and  410   b  store raw flow records  190   a  and  190   b , as described above in  FIG. 2 . The raw flow records  190   a  and  190   b  are each associated with different network devices a and b Likewise, the raw tables  410   a  and  410   b  are each associated with the different network devices a and b and store the raw flow data at the device level. As flow records  190   a  and  190   b  are received in the database  400 , the flow records  190   a  and  190   b  are stored in the raw data tables  410   a  and  410   b . After some trigger event, such as when one of the tables  410   a  and  410   b  becomes full or when a time threshold is achieved, the stored flow records  190   a  and  190   b  in table  410   a  and  410   b  associated with particular devices a or b (or device categories a or b) moved to table  415  for aggregation. 
         [0041]    For example, in one embodiment, data flows records  190   a  and  190   b  from the current time period are stored in current raw tables  410   a  and  410   b . When the flow records  190   a  and  410   b  in either of the current table  410   a  or  410   b  age beyond a predefined threshold, which may be defined according to the storage and access needs of the system  100 , the flow records  190   a  and  190   b  are aggregated as desired as the aggregated flow data  401 , and the current tables  410   a  and  410   b  are reset or a new current tables  410   a  and  410   b  are created using the DDL functions to store new flow records  190   a  and  190   b.    
         [0042]    Continuing with  FIG. 4A , after the data is aggregated in table  415 , the data is re-aggregated according to the time period associated with the flow records, where tables  420   a - 420   n  are flow records associated in time periods a through n and are formed from table  415 . Thus, in the depicted embodiment, each of the tables  410  and  420   a - 420   n , is associated with a different, mutually time period, including the current time period and n prior time periods. As described above, in the current time period, each of the new flow records  190   a - 190   b  is stored in current tables  410   a - 410   b . When the flow records  190   a - 190   b  in the current table  410   a - 410   b  age beyond a predefined threshold, which may be defined according to the storage and access needs of the system  100 , the raw flow records  190   a - 190   b  are aggregated as desired as the aggregated flow data  401  in aggregated table  415  that includes aggregated data records for a current time period. Each of the other aggregated tables  420   a - 420   n  contains, respectively, aggregated flow data  402   a - 402   n  for previous time periods a through. In this way, the aggregation generally occurs along one or more of the above-described data categories within the flow record. [ 0043 ] Continuing with  FIG. 4A , it can be seen that the Flow Record Generating Device  120  may forward new flow records  190   a - 190   b  in the current tables  410   a - 410   b  . At the same time, the Data Analyzer  150  may access the aggregated data  40  and  402   a - 402   n in each of the aggregated tables  415  and  420   a - 420   n . In this way, data contention is minimized because aggregated data may be aggregated as new flow data is added to one of the current tables. Furthermore, because the aggregated data  401  and  402   a - 402   n  is significantly smaller than storing the raw flow records  190   a - 190   b  without any changes, significant storage capacity is freed. Also, the analysis is greatly eased since the aggregated data  401  and  402   a - 402   n  is partially processed. 
         [0043]    As described above, each of the tables  420   a - 420   n  is associated with a time period a-n. The time period are mutually exclusive and may be unique and fixed, or as described below, the time periods may vary as needed. The time periods of the tables  420   a - 420   n  may be used to assign the flow records  190   a - 190   b . In particular, as noted above in Table 1, each of the flow records typically has an associated time stamp. The time stamp for a flow record is compared to for the time periods of the aggregated tables  415  and  420   a - 420   n  to identify an appropriate table. 
         [0044]    In this way, the aggregation generally occurs along one or more of the above-described data categories within the flow record. For example, the aggregated may describe all data of a type or protocol transmitted to or from a particular router a during a time period. 
         [0045]    As described above, the embodiment depicted in  FIG. 4A  generally describes the periodic and cyclical aggregation and storage of the current flow records for each of two network devices in n time periods. It should be appreciated that the duration of the time periods may be defined as needed to accomplish the goals of the data analysis system  100 . For example, data may be aggregated every few minutes, hourly, or daily, as described below in  FIG. 5 . 
         [0046]    The aggregated data  401  and  402   a - 402   n  in the aggregated tables  415  and  420   a - 420   n  may be formed as needed, according to known aggregation techniques. One record in the aggregated data  401  for a time period may include an aggregated flow describing all communications of a particular type during that time period. 
         [0047]    As described above, when the flow records  190  in the current tables  410   a - 410   b  age beyond a predefined threshold or beyond a predefined range of time, which may be defined according to the storage and access needs of the system  100 , the new flow records  190   a - 190   b  are aggregated as desired with other existing aggregated flow records  190  to form an aggregated flow data  401   a - 401 . As before, the aggregation generally occurs along one or more of the above-described data categories within the flow record. For example, the aggregated may describe all data of a type or protocol transmitted to or from a particular router during the prior time periods. The current table  410   a - 410   b  may store records from the latest time period (such as the last fifteen-minute), whereas the aggregated tables  415  and  420   a - 420   n  may store aggregated records from of a longer period, such as the rest of the day. The aggregated prior tables  415  and  420   a - 420   n  may then be periodically cleared, such as once a day, perhaps after the aggregated data is accessed by the analysis server  150 . 
         [0048]    It should be further appreciated that the data may be organized and aggregated in the record storage  140  in different ways as needed. For example, referring to another embodiment of the present invention at  FIG. 4B . A database  400 ′ includes multiple tables  410   a - 410   b  and  420   a - 420   n  that divide the database  400 ′. 
         [0049]    In the depicted embodiment, the tables  410   a - 410   b  are used in the current time period and  420   a - 420   n  correspond to prior time periods. In the current time period, each of the new flow records  190   a - 190   b  is stored in one of the current tables  410   a - 410   b  corresponding to devices a and -b associated with the new flow records  190   a - 190   b . When the flow records  190   a - 190   b  in the current tables  410   a - 410   b  ages beyond a predefined threshold, which may be defined according to the storage and access needs of the system  100 , the new flow records  190   a - 190   b  are aggregated as desired as the aggregated flow data  402   a that aggregates data for a previous time period corresponding to the duration that the current tables  410   a  or  410   b  is used, and the current tables  410   a  and  410   b  thereby are aggregated to become the first aggregated tables  420   a . New current tables  410   a  and  410   b  are created using the DDL functions to store the new flow data  190   a - 190   b . Likewise, each of the other aggregated tables  420   b - 420   n  contains, respectively, aggregated flow data  402   b - 402   n  for previous time periods b-n. 
         [0050]    Another embodiment of the present invention is depicted in  FIG. 5 . Similar to  FIG. 4A , multiple tables  510   a - 510   b ,  515 , and  520   a - 520   n  divide the database  500 . As before, current database stores new flow records  190   a - 190   b , and after a predefined periods, the flow records  190   a - 190   b  may be aggregated according to the devices a and b associated with the records in the aggregated table  515 . This data is then re-aggregated in aggregated tables  520   a - 520   n corresponding to past time periods a through n. In  FIG. 5 , the duration of each of the time period is different from the duration of time period a and increases in duration. For example,  FIG. 5  depicts an example where aggregated table  520   a  correspond to previous period of minutes and table  520   n  covers a period of days. 
         [0051]    Likewise, while it is not depicted in  FIG. 5 , multiple aggregated tables  520   a , may feed data into a single aggregated table of a high aggregation levels. For example, a separate aggregated table may be kept for every hour of a day, and then those hourly aggregated table may be aggregated into a single daily aggregated table. Then, multiple daily aggregated tables may be aggregated into a single weekly aggregated table. 
         [0052]    While the invention has been described with reference to an exemplary embodiments various additions, deletions, substitutions, or other modifications may be made without departing from the spirit or scope of the invention. Accordingly, the invention is not to be considered as limited by the foregoing description, but is only limited by the scope of the appended claims.