Abstract: Systems and methods for data normalization are disclosed. The disclosed systems and methods train one or more machine learning model based on a plurality of annotated records and apply these trained models to new or updated records. The models are configured to analyze the records and generate classifications or concepts from the records and corresponding confidence scores. If the confidence scores exceed a threshold, the corresponding classifications or concepts are appended to the appropriate record.

Abstract: Training risk determination models based on a set of labeled data transactions. A first set of labeled data transactions that have been labeled during a review process is accessed. A first risk determination model is trained using the first set of labeled data transactions. A first risk score for data transactions of a set of unlabeled data transactions is determined using the first risk determination model. Data transactions in the set of unlabeled data transactions are newly labeled based on the first risk score. The newly labeled data transactions are added to a second set of labeled data transactions that include the first set of labeled data transactions. A second risk determination model is trained using at least the second set of labeled data transactions. A second risk score is determined for subsequently received data transactions and these data transactions are rejected or approved based on the second risk score.

Abstract: Embodiments disclosed herein are related to computing systems and methods for determining a risk score for a plurality of data transactions. In the embodiments, a first risk score module may receive data transactions. The first risk score module may then determine a first risk score for each of the data transactions. A second risk score module that is different from the first risk score module may receive each of the first risk scores determined by the first risk score module as an input. The second risk score module may determine a second risk score based in part on the input first risk scores for each of the data transactions. The second risk scores may specify if each of the data transactions is to be approved or rejected by the computing system.

Abstract: Embodiments disclosed herein are related determining a risk score for one or more data transactions. Current data transactions that are associated with one or more current attributes are received. Stored data transactions associated with stored attributes are accessed. A plurality of the stored attributes are selected. A first sliding window and a second sliding window are selected. A duration of the second sliding window is longer than a duration of the first sliding window and encompasses the duration of first sliding window. Risk information for those stored data transactions that are associated with the plurality of attributes is determined. The risk information is determined during the duration of both the first and second sliding windows and is indicative of a level fraud that is occurring. The determined risk information and the current attributes are used to generate a risk score for the current data transactions. The current data transactions are approved or rejected based on the risk score.

Abstract: Training risk determination models based on a set of labeled data transactions. A first set of labeled data transactions that have been labeled during a review process is accessed. A first risk determination model is trained using the first set of labeled data transactions. A first risk score for data transactions of a set of unlabeled data transactions is determined using the first risk determination model. Data transactions in the set of unlabeled data transactions are newly labeled based on the first risk score. The newly labeled data transactions are added to a second set of labeled data transactions that include the first set of labeled data transactions. A second risk determination model is trained using at least the second set of labeled data transactions. A second risk score is determined for subsequently received data transactions and these data transactions are rejected or approved based on the second risk score.

Abstract: Embodiments disclosed herein are related to computing systems and methods for detecting anomalies in a distribution of one or more attributes associated with data transactions. In the embodiments, data transactions are accessed that each include various attributes. The data transactions are grouped into a first subset associated with a first sub-type of a first attribute and a second subset including any remaining sub-types of the first attribute. Second attributes in the first and second subsets are compared to determine differences in the proportion of the second attributes between the first and second subsets, where the differences are indicative of an anomaly in an expected distribution of the second attributes. Based at least on a determination that there are differences in the proportion, subsequently accessed data transactions that are associated with attributes similar to the data transactions of the first subset are rejected or subjected to a further review process.

Abstract: Embodiments disclosed herein are related determining a risk score for one or more data transactions. Current data transactions that are associated with one or more current attributes are received. Stored data transactions associated with stored attributes are accessed. A plurality of the stored attributes are selected. A first sliding window and a second sliding window are selected. A duration of the second sliding window is longer than a duration of the first sliding window and encompasses the duration of first sliding window. Risk information for those stored data transactions that are associated with the plurality of attributes is determined. The risk information is determined during the duration of both the first and second sliding windows and is indicative of a level fraud that is occurring. The determined risk information and the current attributes are used to generate a risk score for the current data transactions. The current data transactions are approved or rejected based on the risk score.

Abstract: Embodiments disclosed herein are related to computing systems and methods for determining a risk score for a plurality of data transactions. In the embodiments, a first risk score module may receive data transactions. The first risk score module may then determine a first risk score for each of the data transactions. A second risk score module that is different from the first risk score module may receive each of the first risk scores determined by the first risk score module as an input. The second risk score module may determine a second risk score based in part on the input first risk scores for each of the data transactions. The second risk scores may specify if each of the data transactions is to be approved or rejected by the computing system.

Abstract: Aspects provided herein are relevant to systems, methods, and techniques for classifying relationships between people (e.g., users of a platform or ecosystem) based on relationship data. In an example, the relationship data can be provided as input into a two-layer classification framework in which the first layer acts a filter for the second layer. The framework can identify relationships such as a self-relationship (e.g., two different accounts on the platform are operated by the same person), a non-self, family-member relationship (e.g., two users are different people but part of the same family), and a non-family-member relationship (e.g., the two users are different people and not part of the same family, such as coworkers or roommates).

Abstract: A resource identifier parser can derive information from resource identifiers that are provided to non-browser application programs or the operating system, such as through the desktop area of a windows-based user interface. The resource identifier parser can understand the formatting of resource identifiers of specific domains and can derive therefrom information such as coordinates of a map page, the title of classified listings, and other like information. If the resource identifier cannot be parsed, the identified data page can be referenced to identify services offered by the page, such as data feed services, which can then be presented to the non-browser application program or the operating system to which the resource identifier was provided. As yet another alternative, the domain can provide a customized resource identifier parser to parse its resource identifiers. Such a customized parser can be either downloadable and locally executable or it can be a network service.

Abstract: In one example, data may be divided into blocks, and the blocks may be stored in various storage resources. Data to be stored may be provided to a data divider. The data divider may divide the data into blocks. Redundancy may be introduced into the blocks to allow the original data to be reconstructed at some point in the future, even if fewer than all of the original blocks are available. The blocks may be sent to a data shuffler, which may shuffle the blocks, and may provide a key that describes how to reconstruct the original data from the blocks. The key may be provided to the owner of the data. When the original data is to be retrieved, the key may be provided to a reassembler, which retrieves the blocks from the various storage resources and reconstructs the data using the key.

Abstract: A state affinitized system comprises worker computing devices uniquely affinitized with one or more states such that only those worker computing devices maintain local copies of the states with which they are affinitized. Requests and data relevant to a state are directed to the worker computing device affinitized with that state, such as by front-end computing devices. A centralized persistent storage can maintain a copy of each state and can modify the states in response to newly received information in the same manner as the worker computing devices. Such maintenance can be performed by coalescer computing devices, which can also derive second order data from the states in the centralized persistent storage, which can be further added to the state and also communicated to the worker computing device affinitized with that state. Crawler computing devices can obtain additional information for incorporation into states from network-based information repositories and services.

Abstract: A system described herein includes a receiver component that receives a computing event with respect to a file stored in a data repository of a personal computing device. An assignor component automatically assigns a uniform resource identifier to the file responsive to the receiver component receiving the computing event with respect to the file, wherein at least a portion of content of the file is reviewable by way of a web browser through utilization of the uniform resource identifier.

Abstract: A system described herein includes a receiver component that receives a computing event with respect to a file stored in a data repository of a personal computing device. An assignor component automatically assigns a uniform resource identifier to the file responsive to the receiver component receiving the computing event with respect to the file, wherein at least a portion of content of the file is reviewable by way of a web browser through utilization of the uniform resource identifier.

Abstract: A database query user interface combines the user convenience of simple text searching with the expressive refinements of powerful query language. The database query user interface includes a query text string input from a user including one or more terms of a chunk expression language format. The database query user interface further includes a syntactical prompt for constructing a multi-element chunk expression language database query that is syntactically correct and complete and includes the text string input from the user. For example, the syntactical prompt is selected from the database based upon a weighted analysis of database information relating to database elements included in the text string input from the user. A database query formed according to the present user interface may then be persisted or stored as a database query object.

Abstract: A protocol is provided for communicating data between two applications. The protocol can support communication of an extensible markup language (XML) document over hypertext transfer protocol (HTTP) by associating the XML document with a data envelope and sending the data envelope to a disparate application. The data envelope can comprise a header, which can specify supported compression methods, encryption keys, and/or data specific to the disparate application and/or the sending application. The protocol adds a layer of security to the communication and can prevent malicious requests and other attacks. This protocol can also be used in conjunction with a health integration network.

Abstract: Data may be stored in a location or manner that takes various considerations into account. Examples of such considerations are the availability, speed and cost of storage resources, and commercial and regulatory expectations concerning the reliability, security, and/or availability of the stored data. When a piece of data is to be stored, a storage choice component may take the above-considerations into account in order to determine where the data is to be stored, how many copies of the data are to be made, whether the data is to be encrypted, and/or other issues. Additionally, a migration engine may re-evaluate data that has already been stored in order to determine whether the data may be migrated to other storage resources, and/or whether changes may be made with regard to issues such as the encryption level and/or the number of stored copies of the data.

Abstract: Systems and/or methods that mitigate overexposure of shared content and that facilitate improving security of content in a computing environment are provided. In particular, the systems and methods involves monitoring resource activities such as user interactions (between users and/or between users and resources), email activity, share space activity, shared content activity, distribution lists, contact lists, personnel or organization structures, and the like. Data can be collected as one or more resource activities are monitored and then analyzed to determine where, if any, changes have taken place. For example, if a user has been removed from a distribution list or a user has not accessed the shared content within a time period, a recommendation can be formulated to suggest an adjustment to the affected access rights. Furthermore, the systems and/or methods can be integrated with other applications such as an email client, whereby an out-of-office indicator can leave access rights as they are.

Abstract: Embodiments are provided for creating a personalized search index and supplementing searches with an additional set of keywords. A first set of keywords is received from a user. A first index associated with the user is searched using the first set of keywords to generate a first set of search results. The first index includes content identified by the user. A second set of keywords is defined based on the first set of search results. A second index is searched using the second set of keywords to generate a second set of search results. At least a portion of the first and second sets of search results is provided to the user. Some embodiments are implemented as a cloud computing service.

Abstract: A resource identifier parser can derive information from resource identifiers that are provided to non-browser application programs or the operating system, such as through the desktop area of a windows-based user interface. The resource identifier parser can understand the formatting of resource identifiers of specific domains and can derive therefrom information such as coordinates of a map page, the title of classified listings, and other like information. If the resource identifier cannot be parsed, the identified data page can be referenced to identify services offered by the page, such as data feed services, which can then be presented to the non-browser application program or the operating system to which the resource identifier was provided. As yet another alternative, the domain can provide a customized resource identifier parser to parse its resource identifiers. Such a customized parser can be either downloadable and locally executable or it can be a network service.