WO2016186638A1

WO2016186638A1 - Detecting an erroneously stored data object in a data container

Info

Publication number: WO2016186638A1
Application number: PCT/US2015/031316
Authority: WO
Inventors: Dave DONAGHY; Ben SIMPSON; John Butt
Original assignee: Hewlett Packard Enterprise Development LP
Current assignee: Hewlett Packard Enterprise Development LP
Priority date: 2015-05-18
Filing date: 2015-05-18
Publication date: 2016-11-24
Anticipated expiration: 2017-11-18

Abstract

Detecting an erroneously stored data object in a data container is disclosed. One example is a system including a data processor, an evaluator module, and an error detection module. The data processor identifies a data object stored in a data container. The evaluator module evaluates a similarity score for the data object and a given data item in the data container, the similarity score indicative of a degree of overlap between contents of the data object and the given data item. The error detection module detects if the data object has been erroneously stored in the data container based on the similarity score.

Description

DETECTING AN ERRONEOUSLY STORED DATA OBJECT

IN A DATA CONTAINER Background

[0001] Data containers are generally utilized to store data that may need to be recovered at another time. Sometimes, data may be erroneously stored in a data container. Such storage error may lead to undesirable consequences including, for example, unauthorized access to data, loss of data, and corruption of existing data in the data container. Brief Description of the Drawings

[0002] Figure 1 is a functional block diagram illustrating one example of a system for detecting an erroneously stored data object in a data container.

[0003] Figure 2 is a block diagram illustrating one example of a computer readable medium for detecting an erroneously stored data object in a data container.

[0004] Figure 3 is a flow diagram illustrating one example of a method for detecting an erroneously stored data object in a data container.

[0005] Figure 4 is a flow diagram illustrating another example of a method for detecting an erroneously stored data object in a data container. Detailed Description

[0006] Data storage products, especially data backup devices, are often used to store large amounts of similar data; similarity of two items in this context is intended to mean that the items share data subsets. In some instances, human error, or system error outside the backup device, may result in a data item being erroneously copied to a device other than the one for which it was intended. This may result in data loss, and/or accidental exposure of the data item to a third party, potentially with serious legal and/or commercial ramifications.

[0007] As described herein, data stored erroneously in a backup device may be automatically detected before a human user may identify the error, by comparing the new data against existing data items in the data store, and in particular by comparing the similarity of the new data to the existing data against the similarity of existing data items to each other.

[0008] As described in various examples herein, detecting an erroneously stored data object in a data container is disclosed. One example is a system including a data processor, an evaluator module, and an error detection module. The data processor identifies a data object stored in a data container. The evaluator module evaluates a similarity score for the data object and a given data item in the data container, the similarity score indicative of a degree of overlap between contents of the data object and the given data item. The error detection module detects if the data object has been erroneously stored in the data container based on the similarity score.

[0009] In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific examples in which the disclosure may be practiced. It is to be understood that other examples may be utilized, and structural or logical changes may be made without departing from the scope of the present disclosure. The following detailed description, therefore, is not to be taken in a limiting sense, and the scope of the present disclosure is defined by the appended claims. It is to be understood that features of the various examples described herein may be combined, in part or whole, with each other, unless specifically noted otherwise.

[0010] Figure 1 is a functional block diagram illustrating one example of a system 100 for detecting an erroneously stored data object in a data container. System 100 includes a data processor 104, an evaluator module 106, and an error detection module 108. The data processor 104 may be communicatively linked to at least one data container 102. The data processor 104 identifies a data object stored in a data container. For example, data container 102 may include a plurality of data items, and the data processor 104 may identify a data object of the plurality of data items. In some examples, the data processor 104 may identify the data object based on metadata associated with the data item. For example, as data items are stored in the data container 102, metadata may be associated with the data items. For example, the metadata may be a timestamp associated with the data item, where the time stamp may be indicative of when the data item was created, received, and/or stored. Also for example, the metadata may be a data size associated with the data item, where the data size is indicative of, for example, a compressed size of the data item.

[0011] The evaluator module 106 may evaluate a similarity score for the data object and a given data item in the data container, the similarity score indicative of a degree of overlap between contents of the data object and the given data item. The similarity score for the data object and the given data item may be utilized to compare each incoming data item with existing data items in the data container 102. In some examples, the similarity score may be a measure of similarity as a mathematical "metric" or“distance”, where such a metric or distance may be defined as a total size of shared chunks of data between two items. In some examples, the evaluator module 106 may generate a metric space structure on the set of all data items in the data container 102.

[0012] In some examples, the evaluator module106 may determine the similarity scores as a distribution of mutual distances between pairs of data items stored in the data container 102. Accordingly, the distances of the data object from the data items stored in the data container 102 may be compared to the distribution of mutual distances, and such a comparison may be utilized to detect if the distances of the data object from the data items is more than a threshold. In some examples, the data object may be identified as an outlier of the distribution of mutual distances. For example, an outlier may be detected based on statistical methods, depth based methods, deviation based methods, distance based methods and/or density based methods.

[0013] The error detection module 108 may detect if the data object has been erroneously stored in the data container based on the similarity score. The term “erroneous” as used herein, generally refers to a potential error in placing the data in a particular data container. For example, data meant to be stored in a first data container may be erroneously directed to a second data container. Also, for example, data meant to be stored in a data container may require authorization for storage. Generally, such storage error may lead to undesirable consequences including, for example, unauthorized access to data, loss of data, and corruption of existing data in the data container.

[0014] In some examples, the error detection module 108 may determine if new items being added to the data container 102 are substantially equidistant with respect to the metric generated by the evaluator module 106. If the data object is not substantially equidistant from existing data items in the data container 102, as existing data items are from each other, then the error detection module 108 may flag the data object as potentially erroneously stored in the data container 102.

[0015] In some examples, the identified data object may be composed of multiple chunks of data. Accordingly, a fit of the data object in the data container 102 may be represented by a subset of a totality of chunks in the data object that are already represented by data items in the data container 102. Such a subset relationship may be captured quantitatively. For example, a size of the subset of chunks may be subtracted from a size of the total collection of chunks. Geometrically, the size of the subset of chunks may be divided by the size of the total set of chunks. Accordingly, in some examples, the evaluator module 106 may determine the similarity scores as a ratio of a number of the data object’s chunks already contained in the data container 102 to the total number of chunks in the data object. In some examples, the evaluator module 106 may determine the similarity scores as a difference between the number of the data object’s chunks already contained in the data container 102 and the total number of chunks in the data object.

[0016] In some examples, the similarity score may exceed a threshold, and the error detection module 108 may determine that the data object has been erroneously stored in the data container 102. In some examples, based on such a determination, system 100 may prevent the data container 102 from releasing the erroneously stored data object, thereby preventing accidental disclosure, deletion, unauthorized access, unauthorized modification, and so forth, until an appropriate authorization has been received.

[0017] In some examples, the error detection module 108 may provide, to a computing device via an interactive graphical user interface, a notification that the data object has been erroneously stored in the data container. Generally, the notification may be a message and/or alert provided via a processor to notify relevant individuals and/or network devices of a status update related to storing in the data container 102. In some examples, the notification may be in the form of a system alert displayed via a system management device, an electronic message provided to a system manager, and/or a text message provided to a system manager. In some examples, the notification may be provided along with at least one selectable menu option to receive input from a user.

[0018] In some examples, the error detection module 108 may receive, via the interactive graphical user interface, a user input responsive to the notification. For example, the user may receive the notification and may acknowledge receipt of the notification. Also, for example, the user may initiate remedial actions based on the notification.

[0019] In some examples, the user input may indicate that the data object has not been erroneously stored in the data container. For example, the user may receive the notification that the data object has been potentially erroneously stored in the data container 102. However, the user may indicate that the data object is properly stored in the data container 102.

[0020] In some examples, the user input may indicate that the data object has been erroneously stored in the data container. For example, the user may receive the notification that the data object has been potentially erroneously stored in the data container 102. However, the user may recognize that the data object was intended to be stored in a second data container. Accordingly, the user may indicate that the data object is erroneously stored in the data container 102. In some examples, the error detection module 108 may receive a second user input indicative of a selection of an additional data container for storage of the data object. For example, the user may indicate that the data object be removed from the data container 102, and be stored in the second data container.

[0021] In some examples, the error detection module 108 may receive a third user input indicative of access restrictions to the contents of the data object. For example, when the data object is erroneously stored in the data container 102, access restrictions may be associated with the data object to prevent unauthorized access to contents of the data object.

[0022] The components of system 100 may be computing resources, each including a suitable combination of a physical computing device, a virtual computing device, a network, software, a cloud infrastructure, a hybrid cloud infrastructure that may include a first cloud infrastructure and a second cloud infrastructure that is different from the first cloud infrastructure, and so forth. The components of system 100 may be a combination of hardware and programming for performing a designated visualization function. In some instances, each component may include a processor and a memory, while programming code is stored on that memory and executable by a processor to perform a designated function.

[0023] For example, the evaluator module106 may be a combination of hardware and software (programming such as computer readable medium with instructions executable by a process to perform functions described herein) to evaluate a similarity score for the data object and a given data item in the data container 102, where the similarity score is indicative of a degree of overlap between contents of the data object and the given data item. For example, the evaluator module 106 may include programming to determine shared elements between the data object and the given data item in the data container 102. The evaluator module 106 may include hardware to physically store, for example, such determined similarity scores.

[0024] Likewise, the error detection module 108 may be a combination of hardware and programming to detect if the data object has been erroneously stored in the data container based on the similarity score. For example, the error detection module 108 may include programming to compare similarity scores to determine if a threshold is exceeded. Also, for example, the error detection module 108 may include programming to provide a notification to a computing device via an interactive graphical user interface. As another example, the error detection module 108 may include programming to process user inputs received via the interactive graphical user interface. The error detection module 108 may include hardware to physically store, for example, an updated lookup table of information related to thresholds and similarity scores. Also, for example, the error detection module 108 may include software programming to dynamically interact with the other components of system 100.

[0025] Generally, the components of system 100 may include programming and/or physical networks to be communicatively linked to other components of system 100. In some instances, the components of system 100 may include a processor and a memory, while programming code is stored and on that memory and executable by a processor to perform designated functions.

[0026] A computing device, as used herein, may be, for example, a web-based server, a local area network server, a cloud-based server, a notebook computer, a desktop computer, an all-in-one system, a tablet computing device, a mobile phone, an electronic book reader, or any other electronic device suitable for provisioning a computing resource to perform a unified visualization interface. The computing device may include a processor and a computer-readable storage medium.

[0027] Figure 2 is a block diagram illustrating one example of a computer readable medium for detecting an erroneously stored data object in a data container. Processing system 200 includes a processor 202, a computer readable medium 208, input devices 204, and output devices 206. Processor 202, computer readable medium 208, input devices 204, and output devices 206 are coupled to each other through a communication link (e.g., a bus).

[0028] Processor 202 executes instructions included in the computer readable medium 208. Computer readable medium 208 includes data object identification instructions 210 to identify, via a processing system, a data object stored in a data container.

[0029] Computer readable medium 208 includes similarity scores determination instructions 212 to evaluate similarity scores between the data object and data items in the data container, the similarity scores indicative of a degree of overlap between contents of the data object and each data item.

[0030] Computer readable medium 208 includes storage error detection instructions 214 to detect if the data object has been erroneously stored in the data container based on the similarity scores. In some examples, the storage error detection instructions 214 include instructions to determine that the data object has been erroneously stored in the data container when the similarity score exceeds a threshold.

[0031] Computer readable medium 208 includes notification providing instructions 216 to provide, to a computing device via an interactive graphical user interface, a notification indicative of whether the data object has been erroneously stored in the data container.

[0032] Computer readable medium 208 includes user input receipt instructions 218 to receive, via the interactive graphical user interface, a user input responsive to the notification.

[0033] Input devices 204 include a keyboard, mouse, data ports, and/or other suitable devices for inputting information into processing system 200. In some examples, input devices 204, such as a computing device, are used to receive the data object. Output devices 206 include a monitor, speakers, data ports, and/or other suitable devices for outputting information from processing system 200. In some examples, output devices 206 are used to provide the notification.

[0034] As used herein, a“computer readable medium” may be any electronic, magnetic, optical, or other physical storage apparatus to contain or store information such as executable instructions, data, and the like. For example, any computer readable storage medium described herein may be any of Random Access Memory (RAM), volatile memory, non-volatile memory, flash memory, a storage drive (e.g., a hard drive), a solid state drive, and the like, or a combination thereof. For example, the computer readable medium 208 can include one of or multiple different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories; magnetic disks such as fixed, floppy and removable disks; other magnetic media including tape; optical media such as compact disks (CDs) or digital video disks (DVDs); or other types of storage devices.

[0035] As described herein, various components of the processing system 200 are identified and refer to a combination of hardware and programming configured to perform a designated visualization function. As illustrated in Figure 2, the programming may be processor executable instructions stored on tangible computer readable medium 208, and the hardware may include processor 202 for executing those instructions. Thus, computer readable medium 208 may store program instructions that, when executed by processor 202, implement the various components of the processing system 200.

[0036] Such computer readable storage medium or media is (are) considered to be part of an article (or article of manufacture). An article or article of manufacture can refer to any manufactured single component or multiple components. The storage medium or media can be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions can be downloaded over a network for execution.

[0037] Computer readable medium 208 may be any of a number of memory components capable of storing instructions that can be executed by Processor 202. Computer readable medium 208 may be non-transitory in the sense that it does not encompass a transitory signal but instead is made up of one or more memory components configured to store the relevant instructions. Computer readable medium 208 may be implemented in a single device or distributed across devices. Likewise, processor 202 represents any number of processors capable of executing instructions stored by computer readable medium 208. Processor 202 may be integrated in a single device or distributed across devices. Further, computer readable medium 208 may be fully or partially integrated in the same device as processor 202 (as illustrated), or it may be separate but accessible to that device and processor 202. In some examples, computer readable medium 208 may be a machine-readable storage medium.

[0038] Figure 3 is a flow diagram illustrating one example of a method for detecting an erroneously stored data object in a data container.

[0039] At block 300, a processing system may identify a data object stored in a data container.

[0040] At block 302, a processing system may evaluate a similarity score for the data object and a given data item in the data container, the similarity score indicative of a degree of overlap between contents of the data object and the given data item.

[0041] At block 304, based on the similarity score, a processing system may detect if the data object has been erroneously stored in the data container.

[0042] At block 306, a processing system may provide, to a computing device via an interactive graphical user interface, a notification indicative of whether the data object has been erroneously stored in the data container.

[0043] In some examples, the method may further include determining that the data object has been erroneously stored in the data container when the similarity score exceeds a threshold. In some examples, the method may further include receiving, via the interactive graphical user interface, a user input responsive to the notification. In some examples, the user input may indicate that the data object has not been erroneously stored in the data container. In some examples, the user input may indicate that the data object has been erroneously stored in the data container.

[0044] Figure 4 is a flow diagram illustrating another example of a method for detecting an erroneously stored data object in a data container.

[0045] At block 400, a processing system may identify a data object stored in a data container.

[0046] At block 402, a processing system may evaluate similarity scores between the data object and data items in the data container.

[0047] At block 404, a processing system may determine if the similarity scores exceed a threshold.

[0048] At block 406, upon a determination that the similarity scores do not exceed the threshold, a processing system may determine that the data object is not erroneously stored.

[0049] At block 408, upon a determination that the similarity scores exceed the threshold, a processing system may provide, to a computing device via an interactive graphical user interface, a notification that the data object has been erroneously stored in the data container.

[0050] At block 410, a processing system may receive user input responsive to the notification. [0051] At block 412, a processing system may determine if the user input confirms that the data object has been erroneously stored in the data container.

[0052] At block 406, upon a determination that the user input does not confirm that the data object has been erroneously stored, a processing system may determine that the data object is not erroneously stored.

[0053] At block 414, upon a determination that the user input confirms that the data object has been erroneously stored, a processing system may determine that the data object is erroneously stored.

[0032] Examples of the disclosure provide a generalized system for detecting an erroneously stored data object in a data container. The generalized system minimizes the impact of an erroneous storage by detecting the erroneously stored data based on similarities with existing data items. The generalized system may further incorporate user interactions related to potential erroneous storage.

[0033] Although specific examples have been illustrated and described herein, a variety of alternate and/or equivalent implementations may be substituted for the specific examples shown and described without departing from the scope of the present disclosure. This application is intended to cover any adaptations or variations of the specific examples discussed herein. Therefore, it is intended that this disclosure be limited only by the claims and the equivalents thereof.

Claims

CLAIMS 1. A system comprising:

a data processor to identify a data object stored in a data container;

an evaluator module to evaluate a similarity score for the data object and a given data item in the data container, the similarity score indicative of a degree of overlap between contents of the data object and the given data item; and

an error detection module to detect if the data object has been erroneously stored in the data container based on the similarity score.

2. The system of claim 1, wherein the similarity score exceeds a threshold, and the error detection module determines that the data object has been erroneously stored in the data container.

3. The system of claim 2, wherein the error detection module is to further provide, to a computing device via an interactive graphical user interface, a notification that the data object has been erroneously stored in the data container.

4. The system of claim 3, wherein the error detection module is to further receive, via the interactive graphical user interface, a user input responsive to the notification.

5. The system of claim 4, wherein the user input indicates that the data object has not been erroneously stored in the data container.

6. The system of claim 4, wherein the user input is a confirmation that the data object has been erroneously stored in the data container.

7. The system of claim 6, wherein the error detection module is to further receive a second user input indicative of a selection of an additional data container for storage of the data object.

8. The system of claim 6, wherein the error detection module is to further receive a third user input indicative of access restrictions to the contents of the data object.

9. A method comprising:

identifying, via a processing system, a data object stored in a data container;

evaluating a similarity score for the data object and a given data item in the data container, the similarity score indicative of a degree of overlap between contents of the data object and the given data item;

detecting, based on the similarity score, if the data object has been erroneously stored in the data container; and

providing, to a computing device via an interactive graphical user interface, a notification indicative of whether the data object has been erroneously stored in the data container.

10. The method of claim 9, further comprising determining that the data object has been erroneously stored in the data container when the similarity score exceeds a threshold.

11. The method of claim 10, further comprising receiving, via the interactive graphical user interface, a user input responsive to the notification.

12. The method of claim 11, wherein the user input indicates that the data object has not been erroneously stored in the data container.

13. The method of claim 11, wherein the user input is a confirmation that the data object has been erroneously stored in the data container.

14. A non-transitory computer readable medium comprising executable instructions to:

identify, via a processing system, a data object stored in a data container; evaluate similarity scores between the data object and data items in the data container, the similarity scores indicative of a degree of overlap between contents of the data object and each data item;

detect if the data object has been erroneously stored in the data container based on the similarity scores;

provide, to a computing device via an interactive graphical user interface, a notification indicative of whether the data object has been erroneously stored in the data container; and

receive, via the interactive graphical user interface, a user input responsive to the notification.

15. The non-transitory computer readable medium, further comprising instructions to determine that the data object has been erroneously stored in the data container when the similarity score exceeds a threshold.