Abstract: The present invention involves a system and method that facilitate extracting data from messages for spam filtering. The extracted data can be in the form of features, which can be employed in connection with machine learning systems to build improved filters. Data associated with the subject line, timestamps, and the message body can be extracted and employed to generate one or more features. In particular, subject lines and message bodies can be examined for consecutive, repeating characters, blobs, the association or distance between such characters, blobs and non-blob portions of the message. The values or counts obtained can be broken down into one or more ranges corresponding to a degree of spaminess. Presence and type of attachments to messages, percentage of non-white-space and non-numeric characters of a message, and determining message delivery times can be used to identify spam. A time-based delta can be computed to facilitate determining the delivery time.

Abstract: A predictive failure model is used to generate a failure prediction associated with a node. A repair or backup action may also be determined to perform on the node based on the failure prediction.

Abstract: A backup system that utilizes contextual and semantic concepts is described. The backup system provides for the ability to create a version changes log for listing and tracking all the changes in the different versions of the file. The version changes log creates a contextual description around the changes, deletions and additions. The semantic concept log is created from the version changes log to create a log of all of the semantic concepts associated with each change. A visualization builder then creates visualizations that can be used by the user to search for changes, deletions and additions whether in a text file or an image file.

Abstract: Embodiments of erasure coded storage aggregation are disclosed. The erasure coded storage aggregation includes storing a data file as erasure coded fragments in a plurality of nodes of one or more data centers. The erasure coded storage aggregation further includes monitoring an access frequency of the data file. Based on the comparison between the access frequency and a predetermined threshold, the data file is either reconstructed from the erasure coded fragments and stored in a storage node or retained as erasure coded fragments in the plurality of nodes of the one or more data centers.

Abstract: Techniques are disclosed for identifying and stashing all or portions of application data to a network accessible storage device or location. Identifying and stashing are accomplished in a fashion that is transparent to a user such that a user is not burdened by the process of managing and stashing a full or partial set of application data. The system, the application, or a combination of system components and the application persist and manage data locally and stash portions of the local data to one or more network accessible storage devices or locations. One or more links are provided in place of the stashed portions of data. The application uses the links in a same or similar fashion as handling local data while providing little to no loss of functionality due to all or portions of the local data being stashed.

Abstract: A predictive failure model is used to generate a failure prediction associated with a node. A repair or backup action may also be determined to perform on the node based on the failure prediction.

Abstract: Techniques for providing content management services are described. Managing content services includes determining a value of content and determining storage actions of the content based on the value. The value of the content is determined by weighting factors, such as access frequency, a rating, a size of the content, a type of the content, and a security level of the content. Based on the value of the content, one or more storage actions may be determined for the content to optimize storage. The storage actions may include identifying a version of the content to be saved, compressing the content, determining a back up of the content to store, identifying a location to store the content, performing erasure encoding of the content, and/or removing the content.

Abstract: The claimed subject matter relates to systems and/or methodologies that facilitate intelligent distribution of backup information across storage locations in network-based backup architectures. A virtual layering of backup information across storage locations in the backup architecture can be implemented. Statistical models are utilized to dynamically re-allocate backup information among storage locations and/or layers to ensure availability of data, minimum latency upon restore, and minimum bandwidth utilization upon restore. In addition, heuristics or machine learning techniques can be applied to proactively detect failures or other changes in storage locations such that backup information can be reallocated accordingly prior to a failure.

Abstract: The claimed subject matter provides a system and/or a method that facilitates integration of a distributed backup environment and a online backup environment. A super peer device can be designated from a set of peer devices. The super peer can distribute backup data amongst the set of peer devices based upon availability and storage capacity of the peer devices. In addition, the super peer can transfer portions of backup data from the set of peers to an online backup service.

Abstract: The claimed subject matter relates to systems and/or methodologies that facilitate distributed storage of data. A distributed file system can be implemented on storage nodes such that the system places multiple copies of data (e.g., replicas) on a variety of disparate storage nodes to guarantee availability of the data and minimize loss of the data. Storage nodes are dynamically evaluated to identify respective characteristics. In one example, the characteristics can include availability of a storage node, capacity of a storage node, data storage cost associated with a storage node, data transfer costs associated with a storage node, locality of a storage node, network topology, or user preferences associated with a storage node. The characteristics can be employed to generate optimal placements decisions.

Abstract: Provided herein are systems and methodologies for highly efficient backup and restoration in a network-based backup system. A distributed, hybrid peer-to-peer (P2P)/cloud backup architecture is leveraged, wherein information can be segmented and distributed across a set of peers and one or more global storage locations (e.g., cloud storage locations) within an associated network or internetwork. Using this architecture, images and/or delta blocks corresponding to respective images are intelligently placed across storage locations based on various network factors such as node locality, health, capacity, or the like. Similarly, restoration of a system can be performed by querying respective locations at which data corresponding to a desired system state are located and pulling the data from one or more optimal network locations as listed in an index and/or a similar structure based on similar network factors.

Abstract: Provided herein are systems and methodologies for highly efficient restoration in a network-based backup system. As described herein, differential-based analysis can be utilized such that a new complete differential is calculated based on signatures and/or other information relating to a given item to be restored prior to retrieving backup data. Based on the differential, only blocks determined to be unique between the current version of the item and the desired version are transmitted, which can then be merged with non-unique locally present blocks to obtain the fully restored version of the item. Further, a hybrid architecture can be employed, wherein signatures and/or data are stored at a global location within a network as well as one or more local peers. Accordingly, a backup client can obtain information necessary for restoration from either the global location or a nearby peer, thus further reducing latency and bandwidth consumption.

Abstract: The claimed subject matter relates to architectures for facilitating network-accessible or local backup or restore features in a manner that leverages event-oriented contextual information associated with one or more users of the data to be backed up or restored. In particular, a first restore-based architecture can interface with an associated second backup-based architecture that hosts or maintains a backup data store in order to retrieve a version of the data file that is desired by a user. Additionally, the first architecture can obtain a log of contextual event descriptions that can be aggregated by the second architecture or obtained independently from, e.g. a social networking service or a calendar application associated with the user.

Abstract: The claimed subject matter provides a system and/or a method that facilitates seamless integration of a data storage service within an operating system (OS). An online safe deposit box can store a portion of data, wherein the online safe deposit box is hosted by a data storage service provider. A local operating system associated with a device can leverage a graphic user interface (GUI) to interact with a portion of data associated with the device. A map component can create a mapped drive that integrates the online safe deposit box within the local operating system to enable seamless data interaction between the local operating system and the online safe deposit box, wherein the data interaction is at least one of a data upload from the local operating system to the online safe deposit box or a data download from the online safe deposit box to the local operating system.

Abstract: The claimed subject matter provides a system and/or a method that facilitates employing a safe deposit box that allows perpetual archiving of data. An online safe deposit box can store a portion of data, wherein the online safe deposit box is hosted by a data storage service provider. An archival model component can implement the online safe deposit box for perpetual archival of the portion of data. The archival model component can ensure at least one of the following: the online safe deposit retains the portion of data for a pre-defined duration; the portion of data within the online safe deposit box is accessible solely by an authorized entity; or the integrity of the portion of data is maintained, wherein the integrity is modified solely by an authorized user.

Abstract: The claimed subject matter relates to a network-accessible, online data archival service with a data store for archiving data for clients of the archival service. The archival service can include an architecture that can facilitate perpetual sustainability and accessibility of data by conforming to a model. In particular, the model can describe or define a minimum set of extensible or pluggable components or modules needed to facilitate and guarantee sustainability of and accessibility to the data in perpetuity.

Abstract: The present invention involves a system and method that facilitate extracting data from messages for spam filtering. The extracted data can be in the form of features, which can be employed in connection with machine learning systems to build improved filters. Data associated with origination information as well as other information embedded in the body of the message that allows a recipient of the message to contact and/or respond to the sender of the message can be extracted as features. The features, or a subset thereof, can be normalized and/or deobfuscated prior to being employed as features of the machine learning systems. The (deobfuscated) features can be employed to populate a plurality of feature lists that facilitate spam detection and prevention. Exemplary features include an email address, an IP address, a URL, an embedded image pointing to a URL, and/or portions thereof.

Abstract: Disclosed are signature-based systems and methods that facilitate spam detection and prevention at least in part by calculating hash values for an incoming message and then determining a probability that the hash values indicate spam. In particular, the signatures generated for each incoming message can be compared to a database of both spam and good signatures. A count of the number of matches can be divided by a denominator value. The denominator value can be an overall volume of messages sent to the system per signature for example. The denominator value can be discounted to account for different treatments and timing of incoming messages. Furthermore, secure hashes can be generated by combining portions of multiple hashing components. A secure hash can be made from a combination of multiple hashing components or multiple combinations thereof. The signature based system can also be integrated with machine learning systems to optimize spam prevention.

Abstract: Techniques are presented for assigning reputations to email senders. In one implementation, real-time statistics and heuristics are constructed, stored, analyzed, and used to formulate a sender reputation level for use in evaluating and controlling a given sender's connection to an message transfer agent or email recipient. A sender with an unfavorable reputation may be denied a connection before resources are spent receiving and processing email messages from the sender. A sender with a favorable reputation may be rewarded by having safeguards removed from the connection, which also saves system resources. The statistics and heuristics may include real-time analysis of traffic patterns and delivery characteristics used by an email sender, analysis of content, and historical or time-sliced views of all of the above.

Abstract: The subject invention provides for a feedback loop system and method that facilitate classifying items in connection with spam prevention in server and/or client-based architectures. The invention makes uses of a machine-learning approach as applied to spam filters, and in particular, randomly samples incoming email messages so that examples of both legitimate and junk/spam mail are obtained to generate sets of training data. Users which are identified as spam-fighters are asked to vote on whether a selection of their incoming email messages is individually either legitimate mail or junk mail. A database stores the properties for each mail and voting transaction such as user information, message properties and content summary, and polling results for each message to generate training data for machine learning systems. The machine learning systems facilitate creating improved spam filter(s) that are trained to recognize both legitimate mail and spam mail and to distinguish between them.