US20040158676A1 - Content-based storage management - Google Patents

Content-based storage management Download PDF

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Publication number
US20040158676A1
US20040158676A1 US10/451,371 US45137103A US2004158676A1 US 20040158676 A1 US20040158676 A1 US 20040158676A1 US 45137103 A US45137103 A US 45137103A US 2004158676 A1 US2004158676 A1 US 2004158676A1
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data
storage
characteristic
rule
optionally
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Yehoshaphat Kasmirsky
Ilan Kor
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Nice Systems Ltd
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Application filed by Individual filed Critical Individual
Assigned to NICE SYSTEMS, LTD. reassignment NICE SYSTEMS, LTD. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: KASMIRSKY, YEHOSHAPHAT, KOR, ILAN
Priority to US10/766,851 priority Critical patent/US7664794B2/en
Publication of US20040158676A1 publication Critical patent/US20040158676A1/en
Priority to US12/697,645 priority patent/US8195616B2/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
    • G06F16/43—Querying
    • G06F16/435—Filtering based on additional data, e.g. user or group profiles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
    • G06F16/48—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F2003/0697—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers device management, e.g. handlers, drivers, I/O schedulers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems

Definitions

  • the present invention relates to a system and a method for content-based storage management, and in particular, for such a system and method in which decisions concerning the location and/or timing of storage of data are based upon the content of the data.
  • on-line storage refers to direct-access, permanently mounted storage areas, such as magnetic (or other types of media) disk drives and disk arrays.
  • the time required for access to such storage areas to be made is typically measured in fractions of a second. Since not all data may need to be stored in on-line storage, which is fast but also expensive, near-line storage is also available.
  • Near-line storage is based upon an automatically (machine) operated storage area, such as optical disks residing on a disk “jukebox” or tapes in an automatic tape library.
  • On-line or near-line storage may also feature a system with a plurality of physical drives, connected together, for example in a LAN (local area network) or WAN (wide area network).
  • LAN local area network
  • WAN wide area network
  • Off-line storage is the least expensive type of storage, but is also the slowest for access, as it does not permit automatic electronic access. Instead, manual operation of the storage devices and physical drives is required by a human operator.
  • the number of physical drives is greatly reduced compared to the number of storage devices (or at least the amount of available storage space). However, the access time for data from such devices is measured from minutes to hours, depending upon the availability of the human operator and the location of the storage devices, as well as the availability of the physical drives.
  • the background art does not teach or suggest a solution to the problem of efficiently managing data storage.
  • the background art also does not teach or suggest a solution to the problem of managing data storage for both cost efficiency and for suitable access times.
  • the background art also does not provide a solution for storing data according to the content of the data, such that important data can be stored in a more accessible location/type of file storage.
  • the background art does not teach or suggest a system and method for managing data, such that data is correctly stored, migrated and/or deleted, according to the content thereof.
  • the present invention overcomes these problems of the background art by providing a system and method for data management according to the content of the data.
  • the present invention enables data to be stored in one of a plurality of different storage options according to at least one characteristic of the data, in which the at least one characteristic is related to the content of the data.
  • at least one storage option according to the present invention is optionally deletion and/or destruction of the data, such that the data may optionally be removed from storage media or may optionally not be stored initially on the storage media. Therefore, a “storage option” according to the present invention includes any type of storage media, device, system or combination thereof, or deletion (removal) of the data.
  • the data is stored for a time interval according to the at least one characteristic of the data.
  • the data is moved to a different type of storage option after an event has occurred, for example after the time interval has elapsed. It should be noted that movement or migration of the data may also include deletion or removal of the data.
  • FIG. 1 is a schematic block diagram of an exemplary system and flow of operations according to the present invention
  • FIG. 2 is a schematic block diagram of another exemplary system and flow of operations according to the present invention.
  • FIG. 3 is a schematic block diagram of a detailed exemplary system according to the present invention.
  • the present invention is of a system and method for data management according to the content of the data.
  • the present invention enables data to be stored in one of a plurality of different storage options according to at least one characteristic of the data, in which the at least one characteristic is related to the content of the data.
  • at least one storage option according to the present invention is optionally deletion and/or destruction of the data, such that the data may optionally be removed from storage media or may optionally not be stored initially on the storage media.
  • the data is stored for a time interval according to the at least one characteristic of the data.
  • the data is moved to a different type of storage option after an event has occurred, for example after the time interval has elapsed. It should be noted that movement or migration of the data may also include deletion or removal of the data.
  • the at least one characteristic of the data, according to which the storage option is selected is examined by a rule engine.
  • the rule engine compares the at least one characteristic of the data to at least one rule, and then selects the storage option (or options) according to that rule.
  • the rule engine therefore more preferably operates as a filter, for determining which storage option(s) is most appropriate for the examined data.
  • the storage decision is then preferably implemented by a storage manager.
  • the rules according to which the rule engine operates are optionally manually entered by a human operator, or alternatively may optionally be generated automatically according to a predefined business rule or according to an automatically generated business rule, or a combination thereof.
  • the present invention is operative with a system featuring a plurality of different storage options. More preferably, these different storage options include at least two different storage options having different types of accessibility. Examples of storage options having different types of accessibility include but are not limited to on-line storage, near-line storage and off-line storage.
  • the type of storage media which is used for any particular storage option is not limited according to the present invention, as the present invention is operable with any suitable type of storage media, including but not limited to DAT (tape-based storage), AIT (also tape-based storage), magnetic storage media, optical disks, CD-ROM or a mass storage device of any type, or any type of storage system, or any combination thereof.
  • the at least one characteristic of the data may optionally and preferably be obtained in a number of different ways.
  • the data may optionally and preferably have associated metadata, which is related to the content of the data.
  • the metadata is more preferably added through annotation of the data itself. Such annotation is optionally performed manually, through human intervention, but is preferably performed automatically. More preferably, automatic annotation is performed after the data is automatically analyzed.
  • the associated metadata is then preferably used to determine which storage option should be used for the data, and more preferably also the time interval during which the data should be placed in that storage option.
  • the data is more preferably filtered by a rules engine, according to at least one characteristic of the data.
  • the filtering process is more preferably performed according to the associated metadata.
  • Automatic analysis of the data is more preferably performed according to the type of data being analyzed.
  • Examples of different types of automatic analysis processes which may optionally be performed include but are not limited to, speech-to-text conversion for voice communication data, a video analyzer for video data, OCR (optical character recognition) for printed matter which has been electronically scanned, image analysis for image data, text analysis for textual data, and analyzers for user interface data.
  • These different types of data analysis processes are preferably performed according to the source of data, which may optionally be any suitable data source.
  • Examples of different types of data sources include but are not limited to, video data, audio data (including also voice communication data such as voice over IP (VOIP) data, streaming audio data and any other type of audio-related data), coded data, e-mail messages and/or attachments, chat and other types of messaging system messages, documents transmitted by facsimile and user interface data.
  • video data audio data (including also voice communication data such as voice over IP (VOIP) data, streaming audio data and any other type of audio-related data), coded data, e-mail messages and/or attachments, chat and other types of messaging system messages, documents transmitted by facsimile and user interface data.
  • audio data including also voice communication data such as voice over IP (VOIP) data, streaming audio data and any other type of audio-related data
  • coded data coded data
  • e-mail messages and/or attachments chat and other types of messaging system messages
  • documents transmitted by facsimile and user interface data documents transmitted by facsimile and user interface data.
  • the present invention is useful for the collection of data about substantially any type of user interface function.
  • user interface functions include but are not limited to any type of GUI window activity; activity with GUI gadgets such as buttons, sliders or any function provided through a GUI window; the display of any image and/or text, including but not limited to Web pages and/or any component thereof; information provided through an audible interface such as a synthesized voice; information provided through the display of video data; and any type of information which is provided through, or otherwise detectable by, the operating system of the user computational device.
  • the data is preferably “migrated” or moved from a first storage option to a second, different storage option after a time interval has expired.
  • the time interval is preferably determined according to the metadata.
  • FIG. 1 shows a first exemplary system 10 for managing data according to the content of the data, with regard to the flow of operations through system 10 .
  • system 10 features at least one input data source 12 .
  • input data sources 12 include but are not limited to, video data, audio data (including also voice communication data such as voice over IP (VOIP) data, streaming audio data and any other type of audio-related data), coded data, e-mail messages and/or attachments, chat and other types of messaging system messages, documents transmitted by facsimile and user interface data.
  • VOIP voice over IP
  • the data is in the form of an event.
  • Each action of the user preferably causes an event to be generated.
  • the event may then optionally form the data to be captured.
  • the captured data is optionally and preferably passed to a format analyzer 14 for rendering the captured data into a common format for analysis.
  • Format analyzer 14 preferably features a plurality of format modules 16 , each of which is suitable for data of a different type of format. For example, if the input data is voice communication data, then preferably a format module 16 converts the voice communication to textual data, for speech-to-text conversion. Different format modules 16 preferably handle other types of input data, as explained in greater detail with regard to FIG. 3 below.
  • the common data format is optionally and preferably textual data.
  • textual data is optionally not further preprocessed by a format module 16 , or alternatively is only minimally processed.
  • Other types of data in different data formats are then preferably converted to textual data by format module 16 , as described with regard to FIG. 3 below.
  • an analysis module 18 preferably analyzes the data, once the data is in the common format. It should be noted that optionally analysis module 18 is able to handle a plurality of different data formats, through a single module or alternatively from a set of such modules (not shown). Preferably, analysis module 18 operates on a plurality of different types and/or sources of data simultaneously, for example as a multi-thread application.
  • textual data is analyzed, as is well known in the art.
  • One non-limiting example of a text analyzer software is the IntelligentMinerTM product (IBM Corp, see http://www-4.ibm.com/software/data/iminer/fortext/tatools.html as of Dec.
  • This product is useful for analyzing text for a number of functions, such as locating information related to a topic, categorization of information and classification of information.
  • Text analyzer tools are generally known in the art for extracting content and/or information related to the subject matter of text, for example according to one or more keywords, concepts or any other type of organization and/or analysis scheme.
  • the uniform format features a uniform data structure, with a plurality of different types and/or categories of information, for example data from screen events and voice data combined in a single file.
  • This uniform data structure preferably is able to contain the different types or categories of characteristics which are of interest for being associated with the data, in order to determine the content of the captured data.
  • Non-limiting examples of a uniform data structure which may optionally be implemented according to the present invention include structures which use XML (extensible mark-up language) or ASF (Advanced Streaming Format, from Microsoft Corp., USA).
  • Analysis module 18 preferably extracts and/or creates, or otherwise determines, at least one characteristic of the captured data, preferably obtained in the uniform format.
  • analysis module 18 obtains the at least one characteristic from the captured data in the form of metadata. This metadata is then optionally stored in a metadata database (not shown, see FIG. 3).
  • analysis module 18 also gives feedback for improving the performance of format analyzer 14 and/or format module 16 , in order to improve the operation of these components.
  • rule engine 20 only receives the at least one characteristic, more preferably in the form of metadata.
  • the captured data is then passed directly to a storage manager 22 .
  • Rule engine 20 more preferably compares the metadata to at least one rule, which is most preferably a business rule specified by a manual human user, or alternatively may optionally be generated automatically according to a predefined business rule or according to an automatically generated business rule, or a combination thereof.
  • one or more rules may be fed to rule engine 20 through an interface 24 , such as a GUI (graphical user interface) for example.
  • interface 24 may optionally be a simple Web browser-based interface.
  • Rule engine 20 determines the type of storage option (or options) according to one or more rules, as selected through the comparison of the metadata (or characteristic) of the captured data to the rule(s). Additionally or alternatively, the output of rule engine 20 is optionally and preferably fed back to format analyzer 14 , and/or format module 16 , and/or analysis module 18 .
  • Rule engine 20 optionally and more preferably determines both the type of storage option (or options), which should be selected for the particular captured data, and also the term of storage. Most preferably, the captured data is initially stored with a first storage option, and then is migrated (moved) to at least one additional storage option after a period of time has elapsed. This period of time is also most preferably determined according to at least one rule by rule engine 20 .
  • Rule engine 20 may optionally perform an action according to a rule and/or event, in which the event may optionally and preferably trigger automatic application of the rule.
  • actions based on rules are given below.
  • One example of actions which are based on rules include but are not limited to manipulations of stored data.
  • the compression of the data may optionally be altered after an event has occurred, such as a period of time has elapsed.
  • a non limiting example of a reason for altering such compression is to enable more rapid playback of the data.
  • Previously stored data may optionally and preferably be updated with business data.
  • Non limiting examples of such data include the addition of social security or identification number, customer identifier, preferred customer status information and so forth.
  • Current transaction data may also optionally and preferably be linked to the previously stored data file.
  • Previously stored data may also optionally be updated.
  • a non limiting example of such linking may optionally be performed by linking transactions performed by a certain high-status or preferred customer to past transaction by that customer.
  • Another event/action example may optionally be performed with multiple mirroring of data, for example by distributing identical data to several destinations and/or pre-defined locations and/or storage options. Such mirroring may optionally be performed for redundancy purposes, for example for security of the stored data, by duplicating to multiple storage locations/options, and/or for general availability reasons.
  • Storage manager 22 preferably then receives the output of rule engine 20 and the captured data.
  • the output of rule engine 20 preferably includes at least one storage option for the captured data.
  • this storage option could be a type of storage media or deletion and/or removal and/or destruction of the captured data. More preferably, the storage option includes a particular storage device 26 into which the captured data should be placed. Alternatively, storage manager 22 could determine the identity of the particular storage device 26 for storing the data.
  • FIG. 2 shows a different flow arrangement of the system of FIG. 1, as another example of a system according to the present invention.
  • system 10 again features a rule engine 40 .
  • rule engine 40 is the initiating process or component for subsequent actions which are performed by system 10 .
  • rule engine 40 takes the input source according to metadata obtained from an analysis module 38 .
  • Rule engine 40 then preferably sends the captured data, or alternatively only selected captured data, to a storage manager 42 .
  • Storage manager 42 sends the captured data to the correct storage option, shown as preferably being a selected storage device 44 , according to a request for action by rule engine 40 .
  • rule engine 40 feeds back the captured data, and/or information about the captured data, into the input sources.
  • Improved metadata may optionally be obtained from analysis module 38 .
  • the user defines a task in rule engine 40 for archiving certain types of information, such as information about specific telephone calls.
  • Rule engine 40 then preferably uses analysis module 38 to select specific data.
  • Analysis module 38 may optionally be implemented as a call management server, for example.
  • the selected specific data may optionally be any one or more of voice data, data captured from user interface actions, video data, an e-mail transaction, facsimile data, VoIP, Web-co browsing data (obtained from two or more users viewing the same Web page(s) through different Web browser processes), or any coded data or any combination of any type of input sources.
  • the data is obtained from input sources 36 , which may optionally be implemented as an input sources logger.
  • the captured data is then transferred into a storage manager 42 .
  • This data can optionally be retrieved as required from storage devices 44 , more preferably directly by using rule engine 40 and/or storage manager 42 . Such retrieved data may then optionally and more preferably be fed into input sources 36 in order for the retrieved data to be widely accessible (available), and/or for further manipulation.
  • FIG. 3 shows an exemplary, detailed implementation of a system according to the present invention, which is related to the flow of operations shown in FIG. 1.
  • a system 50 preferably features a plurality of input data sources 52 .
  • input data sources 52 are shown as optionally and preferably including a video source 54 for video data, an audio source 56 for audio data, a messaging source 58 for e-mail messages (including attachments), instant messaging and/or chat data, and a facsimile source 60 for data which is transmitted by facsimile.
  • the input data from input data sources 52 is preferably then fed to at least one format analyzer 62 for rendering the captured data into a common format for analysis.
  • Format analyzer 62 preferably features a plurality of format modules 74 , each of which is suitable for data of a different type of format.
  • format modules 74 are shown as optionally and preferably including a video analyzer 76 , a text analyzer 78 , an audio analyzer 79 and an OCR (optical character recognition) module 80 .
  • OCR module 80 may optionally be implemented as is well known in the art, for example through the use of OCR software having an algorithm which could easily be selected by one of ordinary skill in the art.
  • Video analysis may optionally be performed by video analyzer 76 as follows.
  • Video data is obtained, for example from a camera as a non-limiting example of video source 54 .
  • a frame-grabber is then preferably used to obtain at least one frame from the video data.
  • the frame is preferably analyzed. More preferably, only a portion of the frame is stored as captured data. For example, if a video camera is used to monitor the entrance to a secure area, then optionally only those frames, or alternatively those portions of each frame, which feature a human subject near the actual entrance are of interest. Additionally or alternatively, changes in the background of each frame may optionally be detected and tracked, as being of interest.
  • One example of a type of analysis which may be performed with the video data is a motion detection algorithm, which is well known in the art.
  • Another example is face recognition algorithms, which are also well known in the art.
  • Non-limiting examples of video analysis algorithms are described at http://www.cs.rochester.edu/u/nelson/research/motion/motion.html as of Dec. 31, 2001, for motion detection algorithms and at http://www-white.media.mit.edu/vismod/demos/facerec as of Dec. 31, 2001, for face recognition algorithms.
  • Such analyses are performed with firmware, such as a DSP (digital signal processor) for example.
  • the results may then optionally be stored as the captured data.
  • the output of format analyzer 62 preferably features at least one characteristic of the captured data which more preferably is metadata, as previously described.
  • the metadata is more preferably stored in a metadata database 82 .
  • a rule engine 84 then preferably analyzes the metadata from metadata database 82 (or alternatively obtained directly from format analyzer 62 ), in order to apply one or more rules to the captured data.
  • Rule engine 84 may optionally be implemented with the Blaze software Advisor product of Blaze Software (see http://www.blazesoft.com/products as of Dec. 31, 2001 for details).
  • Rule engine 84 may also optionally be implemented as a rule/task engine from Nice Systems Ltd. (Ra'anana, Israel), for example based on business data. Regardless of the specific implementation, rule engine 84 preferably operates according to at least one business rule.
  • Rule engine 84 preferably compares the at least one characteristic of the data to at least one rule, and then selects the storage option (or options) according to that rule. Rule engine 84 therefore more preferably operates as a filter, for determining which storage option(s) is most appropriate for the examined data. The storage decision is then preferably implemented by a storage manager 86 .
  • storage manager 86 is able to select from a plurality of different storage options. More preferably, these different storage options include at least two different storage options having different types of accessibility. Examples of storage options having different types of accessibility include but are not limited to on-line storage, near-line storage and off-line storage. The type of storage media which is used for any particular storage option is not limited according to the present invention. For the purposes of illustration only and without any intention of being limiting, storage manager 86 is shown as being able to select from a plurality of storage devices, shown as an on-line storage device 88 and an off-line storage device 90 . Of course, other types of storage devices and/or systems could be used in place of, or in addition to, these examples of storage devices.
  • Rule engine 84 is optionally and preferably able to feed back information to format analyzer 62 , for improving the performance of format analyzer 62 .
  • System 50 could optionally be implemented at a service center which processes services requests from customers remotely, such that the customer is not physically present at the service center. The customer therefore contacts service center personnel, for example through voice communication (such as a telephone call for example), e-mail messages, facsimiles and so forth.
  • a plurality of business rules has been defined and implemented by rule engine 84 , which could optionally include the following rules: a record is kept for every customer contact that refers to a financial transaction for at least three months even if no transaction occurred, and is kept for each contact in which a financial transaction occurred for at least seven years. In addition, the record for each contact resulting in an actual financial transaction is first stored in on-line storage for one month, and then in an off-line storage for the remainder of the term to seven years.
  • audio source 56 As an example of input source 52 .
  • This captured audio data is analyzed, for example in order to determine if the financial transaction occurred during the contact. If such a transaction occurred, then metadata associated with the captured audio data indicates such an occurrence.
  • Format analyzer 62 and particularly audio analyzer 79 , preferably analyzes the captured audio data to obtain such metadata.
  • the data itself from the call is preferably handled according to one or more business rules, which may optionally be defined manually and/or generated automatically, through the operation of rule engine 84 .
  • rule engine 84 then generates an action to be performed by storage manager 86 .
  • storage manager 86 may optionally store the data from the call, migrate the data to a new type of storage, or delete the data, or any other action or any combination of actions which should be performed according to one or more events.
  • rule engine 84 is able to generate one or more instructions for execution by storage manager 86 .
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EP1374028A2 (de) 2004-01-02
DE60206478T2 (de) 2006-07-06
DE60206478D1 (de) 2005-11-10
US7664794B2 (en) 2010-02-16
ATE306102T1 (de) 2005-10-15
HK1061449A1 (en) 2004-09-17
US8195616B2 (en) 2012-06-05
EP1374028B1 (de) 2005-10-05
ES2253531T5 (es) 2018-09-26
ES2253531T3 (es) 2006-06-01
EP1374028B2 (de) 2018-05-16

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