Abstract: In an approach for calculating a probability of a consistency violation of a transaction in a database management system, a processor receives a plurality of transactions within a predetermined time period. A processor identifies a first pattern in a first transaction of the plurality of transactions based on at least an isolation level of the transaction. A processor identifies a second pattern, wherein the second pattern is the cooperation between the first transaction and the second transaction. A processor determines that the first pattern of the first transaction substantially matches a preexisting pattern, wherein the preexisting pattern corresponds to values for a mathematical model for estimating a percentage of transactions in violation of consistency criteria. A processor extracts values from the first transaction based on the determined preexisting pattern. A processor calculates the mathematical model using the values from the first transaction and the values from the preexisting pattern.

Abstract: In an approach for calculating a probability of a consistency violation of a transaction in a database management system, a processor receives a plurality of transactions within a predetermined time period. A processor identifies a first pattern in a first transaction of the plurality of transactions based on at least an isolation level of the transaction. A processor identifies a second pattern, wherein the second pattern is the cooperation between the first transaction and the second transaction. A processor determines that the first pattern of the first transaction substantially matches a preexisting pattern, wherein the preexisting pattern corresponds to values for a mathematical model for estimating a percentage of transactions in violation of consistency criteria. A processor extracts values from the first transaction based on the determined preexisting pattern. A processor calculates the mathematical model using the values from the first transaction and the values from the preexisting pattern.

Abstract: In an approach for calculating a probability of a consistency violation of a transaction in a database management system, a processor receives a plurality of transactions within a predetermined time period. A processor identifies a first pattern in a first transaction of the plurality of transactions based on at least an isolation level of the transaction. A processor identifies a second pattern, wherein the second pattern is the cooperation between the first transaction and the second transaction. A processor determines that the first pattern of the first transaction substantially matches a preexisting pattern, wherein the preexisting pattern corresponds to values for a mathematical model for estimating a percentage of transactions in violation of consistency criteria. A processor extracts values from the first transaction based on the determined preexisting pattern. A processor calculates the mathematical model using the values from the first transaction and the values from the preexisting pattern.

Abstract: In an approach for calculating a probability of a consistency violation of a transaction in a database management system, a processor receives a plurality of transactions within a predetermined time period. A processor identifies a first pattern in a first transaction of the plurality of transactions based on at least an isolation level of the transaction. A processor identifies a second pattern, wherein the second pattern is the cooperation between the first transaction and the second transaction. A processor determines that the first pattern of the first transaction substantially matches a preexisting pattern, wherein the preexisting pattern corresponds to values for a mathematical model for estimating a percentage of transactions in violation of consistency criteria. A processor extracts values from the first transaction based on the determined preexisting pattern. A processor calculates the mathematical model using the values from the first transaction and the values from the preexisting pattern.

Abstract: Predicting future utilization of a resource. The predicting future utilization of a resource may include receiving inquiries for a potential future utilization of the resource for different future points in time, recording time-based patterns of the received inquiries, recording a historic actual utilization value of the resource, and determining a future utilization of the resource using the recorded time-based patterns of the received inquiries, the recorded historic actual utilization value of the resource and a current inquiry pattern of the resource.

Abstract: Predicting future utilization of a resource. The predicting future utilization of a resource may include receiving inquiries for a potential future utilization of the resource for different future points in time, recording time-based patterns of the received inquiries, recording a historic actual utilization value of the resource, and determining a future utilization of the resource using the recorded time-based patterns of the received inquiries, the recorded historic actual utilization value of the resource and a current inquiry pattern of the resource.

Abstract: In an approach for optimizing read/write access to a document object model in a web browser, one or more computer processors detect one or more accesses to a first data structure of a document object model. Based on the detected one or more accesses, the one or more computer processors determine whether the first data structure is a highly dynamic part of the document object model. Responsive to determining the first data structure is a highly dynamic part of the document object model, the one or more computer processors trigger a conversion of the first data structure to an access optimized data structure. The one or more computer processors convert the first data structure to an access optimized data structure.

Abstract: In an approach to pointer positioning, a computing device receives an instruction to enter a Move Mode. The computing device magnifies, in Move Mode, screen content to generate a virtual display. The computing device displays a first portion of the virtual display and a fixed pointer on the virtual display. The computing device receives physical movement data. The computing device analyzes the physical movement data. The computing device displays a second portion of the virtual display based on the analyzed physical movement data. The computing device receives an instruction to leave Move Mode. The computing device deactivates Move Mode.

Abstract: In an approach to pointer positioning, a computing device receives an instruction to enter a Move Mode. The computing device magnifies, in Move Mode, screen content to generate a virtual display. The computing device displays a first portion of the virtual display and a fixed pointer on the virtual display. The computing device receives physical movement data. The computing device analyzes the physical movement data. The computing device displays a second portion of the virtual display based on the analyzed physical movement data. The computing device receives an instruction to leave Move Mode. The computing device deactivates Move Mode.

Abstract: Ensuring integrity of a displayed web page in a client-server infrastructure including defining an enhancement for a web page markup for an enhanced web page content including a provided web page content and at least one identified provider added content as prerequisite content for the provided content, implementing a secure web browser document object model (DOM) enhancement and an enhanced document object model (DOM) processing embedding a user-requested content and the at least one corresponding prerequisite content based on the web page markup enhancement so that the requested content and the at least one prerequisite content of the enhanced web page content of an enhanced web page are secured and displayed as entity.

Abstract: A method includes associating a state identifier with a page of a user interface; associating an element identifier with an element of the page of the user interface; and tracking a state of the element based on the state identifier and the element identifier throughout a user session of the user interface.

Abstract: Backup management of software environments in a distributed network environment includes: Creating a platform specific software environment according to a predefined software environment definition by a user in the network environment; automatically creating a generalized backup of the software environment by extracting platform independent data contents and platform specific transformation rules; automatically calculating enhanced meta data describing the generalized backup; assigning the enhanced metadata with the predefined software environment definition; registering the generalized backup with the enhanced metadata in a backup storage accessible by a plurality of distributed users connected via said network environment, creating a software environment according to a predefined software environment definition by automatically checking the backup storage for an available generalized backup by means of the predefined software environment definition.

Abstract: Backup management of software environments in a distributed network environment includes: Creating a platform specific software environment according to a predefined software environment definition by a user in the network environment; automatically creating a generalized backup of the software environment by extracting platform independent data contents and platform specific transformation rules; automatically calculating enhanced meta data describing the generalized backup; assigning the enhanced metadata with the predefined software environment definition; registering the generalized backup with the enhanced metadata in a backup storage accessible by a plurality of distributed users connected via said network environment, creating a software environment according to a predefined software environment definition by automatically checking the backup storage for an available generalized backup by means of the predefined software environment definition.

Abstract: A method includes associating a state identifier with a page of a user interface; associating an element identifier with an element of the page of the user interface; and tracking a state of the element based on the state identifier and the element identifier throughout a user session of the user interface.

Abstract: A method for handling e-mail communication by an e-mail server is provided. The method includes receiving an e-mail by the e-mail server, parsing the e-mail to identify a large content item within the e-mail, generating a modified e-mail by replacing the large content item within the e-mail with an identifier, storing the large content item and the large content item forms a basis for a weblog associated with the identifier, sending the modified e-mail, and receiving a reply e-mail to the modified e-mail and the content of the reply e-mail builds an extension to the weblog.

Abstract: In an approach for calculating a probability of a consistency violation of a transaction in a database management system, a processor receives a plurality of transactions within a predetermined time period. A processor identifies a first pattern in a first transaction of the plurality of transactions based on at least an isolation level of the transaction. A processor identifies a second pattern, wherein the second pattern is the cooperation between the first transaction and the second transaction. A processor determines that the first pattern of the first transaction substantially matches a preexisting pattern, wherein the preexisting pattern corresponds to values for a mathematical model for estimating a percentage of transactions in violation of consistency criteria. A processor extracts values from the first transaction based on the determined preexisting pattern. A processor calculates the mathematical model using the values from the first transaction and the values from the preexisting pattern.

Abstract: One or more processors apply rules to a first query to generate a modified query such that processing of the modified query is improved for a first set of processing conditions. One or more processors measure a degree of latency experienced during processing of the modified query under a second set of processing conditions. One or more processors generate other rules to be applied to queries based, at least in part, on the measured degree of latency.

Abstract: One or more processors apply rules to a first query to generate a modified query such that processing of the modified query is improved for a first set of processing conditions. One or more processors measure a degree of latency experienced during processing of the modified query under a second set of processing conditions. One or more processors generate other rules to be applied to queries based, at least in part, on the measured degree of latency.

Abstract: One or more processors apply rules to a first query to generate a modified query such that processing of the modified query is optimized for a first set of processing conditions. One or more processors measure a degree of latency experienced during processing of the modified query under a second set of processing conditions. One or more processors generate other rules to be applied to queries based, at least in part, on the measured degree of latency.

Abstract: Predicting future utilization of a resource. The predicting future utilization of a resource may include receiving inquiries for a potential future utilization of the resource for different future points in time, recording time-based patterns of the received inquiries, recording a historic actual utilization value of the resource, and determining a future utilization of the resource using the recorded time-based patterns of the received inquiries, the recorded historic actual utilization value of the resource and a current inquiry pattern of the resource.