Abstract: A computer hardware-implemented method, system, and/or computer program product prevents a cascading failure in a complex stream computer system causing an untrustworthy output from the complex stream computer system. Multiple upstream subcomponents in a complex stream computer system generate multiple outputs, which are used as inputs to a downstream subcomponent. An accuracy value is assigned to each of the multiple outputs from the upstream subcomponents, and weighting values are assigned to each of the inputs to the downstream subcomponent. The accuracy values and weighting values are utilized to dynamically adjust inputs to the downstream subcomponent until an output from the downstream subcomponent meets a predefined trustworthiness level.

Abstract: A computer hardware-implemented method, system, and/or computer program product matches a current patient to a specific patient readmission cohort. The specific patient readmission cohort, made up of patients having a shared attribute, has a historical likelihood of hospital readmission within a predefined post-discharge length of time for members of the specific patient readmission cohort. A database describing a current patient is selected, based on the cost and speed of accessing that database, as well as the probability that the database describes a similar attribute for the current patient as the shared attribute in the specific patient readmission cohort. If the current patient meets the requisite criteria for entry into the specific patient readmission cohort, then a recommendation designed to reduce a likelihood of hospital readmission of the current patient is generated.

Abstract: A processor-implemented method, system, and/or computer program product securely accesses a specific data store. A non-contextual data object is associated with a context object to define a first synthetic context-based object. The non-contextual data object ambiguously describes multiple types of persons, and the context object provides a circumstantial context that identifies a specific type of person from the multiple types of persons. The first synthetic context-based object is associated with at least one specific data store in a data structure. A string of binary data that describes a requester of data is received by a security module for generating a new synthetic context-based object. If there is a match between the new synthetic context-based object and the first synthetic context-based object, then the data is returned to the requester.

Abstract: A computer implemented method optimizes a configuration of an information technology (IT) infrastructure. A processor establishes a technology rule set, which defines technology weights of an IT infrastructure by mapping capabilities of IT infrastructure components to IT infrastructure attributes needed to execute a specific workload. A candidate IT infrastructure for executing the specific workload is then configured. The processor then receives responses, provided by a customer, to an attribute questionnaire. Each question in the attribute questionnaire is mapped to an IT infrastructure attribute of the candidate IT infrastructure, and the responses define a customer's level of agreement/disagreement with the technology weights. In response to the processor determining that the candidate IT infrastructure fails to meet the customer's expectations, the candidate IT infrastructure is reconfigured until the customer's expectations are met.

Abstract: A processor-implemented method, system, and/or computer program product defines multiple context-based data gravity wells on a context-based data gravity wells membrane. Non-contextual data objects are associated with context objects to define synthetic context-based objects. The synthetic context-based objects are parsed into an n-tuple that includes a pointer to one of the non-contextual data objects, a probability that a non-contextual data object has been associated with a correct context object, and a weighting factor of importance of the synthetic context-based object. A virtual mass of each parsed synthetic context-based object is calculated, in order to define a shape of multiple context-based data gravity wells that are created when synthetic context-based objects are pulled into each of the context-based data gravity well frameworks on a context-based data gravity wells membrane.

Abstract: A Lab On a Chip (LOC) has a Sample Preparation Module (SPM) coupled to a sample inlet, a microchannel coupled to the SPM, and an optic module optically proximate to the microchannel. The optic module holds multiple lenses, each of which has a different effective focal length, such that all fields of focus within the microchannel are covered as objects suspended within the liquid sample pass through the microchannel.

Abstract: A method, computer program product and system for generating and maintaining synthetic context events. The steps include searching a data structure of synthetic context-based objects and associated data for a pattern of context exhibited at a first specified frequency within a first specified time period; combining the synthetic context-based objects and associated data exhibiting the pattern of context exhibited at the first specified frequency within the first specified time period into a synthetic context event; and optimizing and maintaining the synthetic context event by searching the data structure for additional synthetic context-based objects and associated data exhibiting a same pattern of context at a second specified time period different than the first specified time period and adding the additional synthetic context-based objects and associated data to the synthetic context event.

Abstract: A method, system, and computer program product for minimizing surprisal context data. The method comprising the steps of: identifying characteristics of a data event; receiving an input of rank of at least two identified characteristics of the data event; the computer generating a hierarchy of ranked, identified characteristics based on the rank of the at least two identified characteristics of the data event; and comparing the hierarchy of ranked, identified characteristics to a repository of reference artifacts arranged in characteristic context patterns. If at least one reference artifact arranged in characteristic context patterns from the repository matches the hierarchy of ranked, identified characteristics, the characteristic context pattern is stored as a surprisal context filter and compared to a data input of data events to detect anomalous events.

Abstract: A method, system, and computer program product for detecting anomalous events from a data input comprising a plurality of events. The method comprising the steps of: selecting at least one filter selecting for context data determined to be probabilistically present within a specified degree of certainty in the data input; comparing the data input to the selected at least one filter; discarding the events from the data input that are the same as the context data for which the at least one filter selects; and storing in a repository the events remaining in the data input as anomalous events.

Abstract: A method, system, and computer program product for minimizing surprisal context data. The method includes the steps of: identifying characteristics of a data event; receiving an input of rank of at least two identified characteristics of the data event; generating a hierarchy of ranked, identified characteristics based on the rank of the identified characteristics of the data event; and comparing the hierarchy of ranked, identified characteristics to a repository of characteristic context patterns. If at least one reference artifact of the characteristic context pattern matches the hierarchy of ranked, identified characteristics, the characteristic context pattern is broken into pieces, storing the pieces that matched the hierarchy. The stored pieces are then combined into a surprisal context filter pattern with context determined to be probabilistically present within a specified degree of certainty in a data input and compared to a data input of data events to detect anomalous events.

Abstract: A computer hardware-implemented method, system, and/or computer program product identifies non-synthetic event elements in electronic files. A first set of binary data that describes a synthetic event is received from a requesting computer. The synthetic event is a non-executable descriptor of a set of context-related factors. A processor performs a context-based search of a database of electronic files to identify a relevant electronic file, which includes the synthetic event. The processor then searches the relevant electronic file for at least one non-synthetic event element, which is absent from the synthetic event. In response to determining that the relevant electronic file comprises the non-synthetic event element, the processor transmits a second set of binary data to the requesting computer. This second set of binary data includes the relevant electronic file and a description of an identified non-synthetic event element in the relevant electronic file.

Abstract: A device for generating a born encrypted optical file includes a photovoltaic matrix for converting an optical image into a digital file. The digital file is a collection of digital data that has not been processed by any image processing logic and thus cannot be used to directly generate a reproduced image of the object. An encryption logic converts the digital file into an encrypted digital file that can be exported from the device to an authorized device to create a decrypted digital file. This decrypted digital file is capable of being used by a display logic to display an image of the object.

Abstract: A rules engine assigns a set of appropriateness rules to a particular data collection. The set of appropriateness rules describes an optimum usage for the particular data collection based on a source of the particular data collection and a type of activity that is deemed best suited to utilize the particular data collection. The set of appropriateness rules also requires that a data collection requester's role matches a data collection provider's role. In response to determining that a requesting computer is implementing an activity that matches the set of appropriateness rules, a rules engine directs a database controller to transmit the particular data collection to the requesting computer.

Abstract: A computer hardware-implemented method, system, and/or computer program product identifies a null set of synthetic event containing electronic files in a database of electronic files. A synthetic event, which is a non-executable descriptor of a set of context-related factors, is created. A context-based search of a database of electronic files is performed to identify a synthetic event containing electronic file that includes the synthetic event. In response to determining that there are no electronic files in the database of electronic files that contain the synthetic event, a set of binary data is transmitted/broadcast. The set of binary data includes a notice that there are no synthetic event electronic files in the database of electronic files.

Abstract: A system comprises a hashing logic, which executes instructions to convert raw data into a first logical address and payload data, where the first logical address describes metadata about the payload data. A hardware translation unit executes instructions to translate the first logical address into a first physical address on a storage device. A hardware load/storage unit stores the first logical address and the payload data at the first physical address on the storage device. A hardware exclusive OR (XOR) unit compares two logical address vectors to derive a Hamming distance between the two logical address vectors. A hardware retrieval unit retrieves other payload data that is stored at a second physical address whose second logical address is within a predefined Hamming distance from the first logical address, thus allowing payload data from the two logical addresses to be grouped/associated with one another.

Abstract: A method and/or system sorts data into data gravity wells on a data gravity wells membrane. A hashing logic executes instructions to convert raw data into a first logical address and first payload data, wherein the first logical address describes metadata about the first payload data. A hardware XOR unit compares the first logical address to a second logical address to derive a Hamming distance between the first and second logical addresses, wherein the second logical address is for a second payload data. A hardware data vector generator creates a data vector for the second payload data, wherein the data vector comprises the Hamming distance between the first and second logical addresses. A hardware data vector sorter then sorts data vectors into specific hardware data gravity wells on a data gravity wells membrane according to the Hamming distance stored in the data vector.

Abstract: Methods, systems and computer program products for associating healthcare records in healthcare databases with an individual are provided. One or more attribute types are located in first and second records. The one or more attribute types being associated with the individual. A weighted score is determined for the located at least one attribute type based on a comparison of the located at least one attribute type in the first record and the located at least one attribute type in the second record. The weighted score indicates a likelihood that the located at least one attribute type in the first and second records are both associated with the individual. A confidence is provided and is associated with the first and second records based on the weighted score. The confidence score indicates a likelihood that both the first and second records are associated with the individual.

Abstract: A method, system and computer program product for maximizing the utility of data obtained from multiple intersecting data structures and stored in a multi-dimension information space. The method includes the steps of generating a rigid mathematical structure within the multi-dimensional information space; dividing the rigid mathematical structure into segments, each segment having a volume determined by a time of access to the segment relative to an event, a duration of access to the segment and a quantity of data in the segment; and determining a sellable price point for each segment of the rigid mathematical structure based on the volume of the segment.

Abstract: A method, computer program product and system for reconciling a plurality of surprisal data sets of a genetic sequence of an organism being generated from a surprisal data reference genome using a base reference genome. If the base reference genome is not the surprisal data reference genome indicated in the surprisal data set, the surprisal data reference genome is retrieved and compared to the base reference genome to obtain reference genome differences. If a starting location of an instance of the surprisal data set is present in the reference genome differences, the nucleotides of the instance of the surprisal data are compared to the nucleotides of the reference genome difference. If the nucleotides of the instance of the surprisal data are the same as the nucleotides of the reference genome difference, the instance of surprisal data is removed from the surprisal data set.

Abstract: A processor-implemented method, system, and/or computer program product generates and utilizes synthetic context-based objects. A non-contextual data object is associated with a context object, which comports with a predetermined set of constraints, to define a synthetic context-based object, where the non-contextual data object ambiguously relates to multiple subject-matters, and where the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the non-contextual data object. The synthetic context-based object is then associated with at least one specific data store, which includes data that is associated with data contained in the non-contextual data object and the context object. A request for a data store that is associated with the synthetic context-based object results in the return of at least one data store that is associated with the synthetic context-based object.