Abstract: Techniques for managing erasure of data are presented. In response to receiving a request for erasure of data from a set of data stores, an erasure component can analyze a set of rules and information relating to the user account, including an account status and erasure hold status associated with the user account. The set of rules can be based on legal or contractual obligations applicable to the set of data stores, and can indicate various conditions under which data associated with a user account of a user can be eligible to be erased from the set of data stores or an associated data vault repository. The erasure component can determine eligibility for erasure of all or a portion of the set of data from the set of data stores based on the analysis results. Erasure component can determine erasure eligibility scores to pre-qualify user accounts for erasure eligibility.

Abstract: Techniques for data lifecycle discovery and management are presented. Data lifecycle discovery platform (DLDP) can identify data of users, data type, and language of data stored in data stores (DSs) of entities based on scanning of data from databases. DLDP determines compliance of DLDP and DSs with obligations relating to data protection arising out of jurisdictional laws or agreements. DLDP generates rules to facilitate complying with and enforcing laws and agreements. DLDP can determine, and present to authorized users, risk scores relating to levels of compliance of the DLDP, associated platforms, or entities, risk indicator metrics, or a privacy health index of the organization associated with DLDP. DLDP can manage user rights regarding data, and access to data in DSs and information relating thereto stored in secure data store of DLDP. DLDP can remediate issues involving anomalies indicating non-compliance. DLDP can utilize machine learning to enhance various functions of DLDP.

Abstract: Techniques for data lifecycle discovery and management are presented. Data lifecycle discovery platform (DLDP) can identify data of users, data type, and language of data stored in data stores (DSs) of entities based on scanning of data from databases. DLDP determines compliance of DLDP and DSs with obligations relating to data protection arising out of jurisdictional laws or agreements. DLDP generates rules to facilitate complying with and enforcing laws and agreements. DLDP can determine, and present to authorized users, risk scores relating to levels of compliance of the DLDP, associated platforms, or entities, risk indicator metrics, or a privacy health index of the organization associated with DLDP. DLDP can manage user rights regarding data, and access to data in DSs and information relating thereto stored in secure data store of DLDP. DLDP can remediate issues involving anomalies indicating non-compliance. DLDP can utilize machine learning to enhance various functions of DLDP.

Abstract: Techniques for managing erasure of data are presented. In response to receiving a request for erasure of data from a set of data stores, an erasure component can analyze a set of rules and information relating to the user account, including an account status and erasure hold status associated with the user account. The set of rules can be based on legal or contractual obligations applicable to the set of data stores, and can indicate various conditions under which data associated with a user account of a user can be eligible to be erased from the set of data stores or an associated data vault repository. The erasure component can determine eligibility for erasure of all or a portion of the set of data from the set of data stores based on the analysis results. Erasure component can determine erasure eligibility scores to pre-qualify user accounts for erasure eligibility.

Abstract: Techniques for data lifecycle discovery and management are presented. Data lifecycle discovery platform (DLDP) can identify data of users, data type, and language of data stored in data stores (DSs) of entities based on scanning of data from databases. DLDP determines compliance of DLDP and DSs with obligations relating to data protection arising out of jurisdictional laws or agreements. DLDP generates rules to facilitate complying with and enforcing laws and agreements. DLDP can determine, and present to authorized users, risk scores relating to levels of compliance of the DLDP, associated platforms, or entities, risk indicator metrics, or a privacy health index of the organization associated with DLDP. DLDP can manage user rights regarding data, and access to data in DSs and information relating thereto stored in secure data store of DLDP. DLDP can remediate issues involving anomalies indicating non-compliance. DLDP can utilize machine learning to enhance various functions of DLDP.

Abstract: Techniques for data lifecycle discovery and management are presented. Data lifecycle discovery platform (DLDP) can identify data of users, data type, and language of data stored in data stores (DSs) of entities based on scanning of data from databases. DLDP determines compliance of DLDP and DSs with obligations relating to data protection arising out of jurisdictional laws or agreements. DLDP generates rules to facilitate complying with and enforcing laws and agreements. DLDP can determine, and present to authorized users, risk scores relating to levels of compliance of the DLDP, associated platforms, or entities, risk indicator metrics, or a privacy health index of the organization associated with DLDP. DLDP can manage user rights regarding data, and access to data in DSs and information relating thereto stored in secure data store of DLDP. DLDP can remediate issues involving anomalies indicating non-compliance. DLDP can utilize machine learning to enhance various functions of DLDP.

Abstract: Techniques for data lifecycle discovery and management are presented. Data lifecycle discovery platform (DLDP) can identify data of users, data type, and language of data stored in data stores (DSs) of entities based on scanning of data from databases. DLDP determines compliance of DLDP and DSs with obligations relating to data protection arising out of jurisdictional laws or agreements. DLDP generates rules to facilitate complying with and enforcing laws and agreements. DLDP can determine, and present to authorized users, risk scores relating to levels of compliance of the DLDP, associated platforms, or entities, risk indicator metrics, or a privacy health index of the organization associated with DLDP. DLDP can manage user rights regarding data, and access to data in DSs and information relating thereto stored in secure data store of DLDP. DLDP can remediate issues involving anomalies indicating non-compliance. DLDP can utilize machine learning to enhance various functions of DLDP.

Abstract: Aspects of the present disclosure involve, a customizable system and infrastructure which can receive privacy data from varying data sources for privacy scanning, containment, and reporting. In one embodiment, data received is scanned for privacy data extraction using various data connectors and decryption techniques. In another embodiment, the data extracted is transferred to a privacy scanning container where the data is analyzed by various deep learning models for the correct classification of the data. In some instances, the data extracted may be unstructured data deriving form emails, case memos, surveys, social media posts, and the like. Once the data is classified, the data may be stored or contained according to the classification of the data. Still in another embodiment, the classified data may be retrieved by an analytics container for use in reporting.

Abstract: An impression chamber (1) for a 3D printer (6) adapted to receive a high performance plastic filament (19) and including a print head (7) and a printing bed (8), wherein the impression chamber (1) includes a thermally insulated plate (2) and a polyimide film (3) attached to the plate (2) for delimiting a printing space (20). The plate (2) is dimensioned to have a surface (2a) equal or larger than the major surface of the piece (5) to be printed, and the film (3) is dimensioned to surround the printing bed (8). The plate (2) is provided with a first through-hole (15) for allowing the passage of at least part of the print head (7), so that the plate (2) is moved by the movement of the print head (7) and the film (3) is dragged by said movement providing a flexible impression chamber (1).

Abstract: Aspects of the present disclosure involve, a customizable system and infrastructure which can receive privacy data from varying data sources for privacy scanning, containment, and reporting. In one embodiment, data received is scanned for privacy data extraction using various data connectors and decryption techniques. In another embodiment, the data extracted is transferred to a privacy scanning container where the data is analyzed by various deep learning models for the correct classification of the data. In some instances, the data extracted may be unstructured data deriving form emails, case memos, surveys, social media posts, and the like. Once the data is classified, the data may be stored or contained according to the classification of the data. Still in another embodiment, the classified data may be retrieved by an analytics container for use in reporting.

Abstract: Aspects of the present disclosure involve, a customizable system and infrastructure which can receive privacy data from varying data sources for privacy scanning, containment, and reporting. In one embodiment, data received is scanned for privacy data extraction using various data connectors and decryption techniques. In another embodiment, the data extracted is transferred to a privacy scanning container where the data is analyzed by various deep learning models for the correct classification of the data. In some instances, the data extracted may be unstructured data deriving form emails, case memos, surveys, social media posts, and the like. Once the data is classified, the data may be stored or contained according to the classification of the data. Still in another embodiment, the classified data may be retrieved by an analytics container for use in reporting.

Abstract: An impression chamber (1) for a 3D printer (6) adapted to receive a high performance plastic filament (19) and including a print head (7) and a printing bed (8), wherein the impression chamber (1) includes a thermally insulated plate (2) and a polyimide film (3) attached to the plate (2) for delimiting a printing space (20). The plate (2) is dimensioned to have a surface (2a) equal or larger than the major surface of the piece (5) to be printed, and the film (3) is dimensioned to surround the printing bed (8). The plate (2) is provided with a first through-hole (15) for allowing the passage of at least part of the print head (7), so that the plate (2) is moved by the movement of the print head (7) and the film (3) is dragged by said movement providing a flexible impression chamber (1).

Abstract: Methods and systems for generating intelligent actionable information based on tracking and monitoring movements and lineage data across multiple database nodes within an enterprise system are described herein. Data within an enterprise system may be collected and monitored to generate real-time intelligent analytics and predictive insights for presentation. The changes and movements of each data across multiple database nodes within the enterprise system may be monitored, and deviations from a scheduled data flow associated with the data may be traced. Based on the monitoring and tracking of the changes and lineage of the data, performance metrics may be generated along with predictions and prescriptions to improve them. The performance metrics may be visualized via one or more performance reports in response to a user request.