Abstract: A method for continuously tracking business performance impact of order sourcing systems and algorithms that decide how ecommerce orders should be fulfilled by assigning the items of the order to nodes in a fulfillment network such as stores, distribution centers, and third party logistics—to provide automatic root cause analysis and solution recommendations to pre-defined business problems arising from KPI monitoring. A Business Intelligence (BI) dashboard architecture operates with: 1) a monitoring module that continuously monitors business KPIs and creates abnormality alerts; and 2) a root cause analysis module that is designed specifically for each business problem to give real time diagnosis and solution recommendation. The root cause analysis module receives the created alert, and triggers conducting a root cause analysis at an analytics engine. The BI dashboard and user interface enables visualization of the KPI performance and root cause analysis results.

Abstract: A system, method and computer program product for continuously tracking business performance impact of order sourcing systems and algorithms that decide how ecommerce orders should be fulfilled by assigning the items of the order to nodes in a fulfillment network such as stores, distribution centers, and third party logistics—to provide automatic root cause analysis and solution recommendations to pre-defined business problems arising from KPI monitoring. A Business Intelligence (BI) dashboard architecture operates with: 1) a monitoring module that continuously monitors business KPIs and creates abnormality alerts; and 2) a root cause analysis module that is designed specifically for each business problem to give real time diagnosis and solution recommendation. The root cause analysis module receives the created alert, and triggers conducting a root cause analysis at an analytics engine. The BI dashboard and user interface enables visualization of the KPI performance and root cause analysis results.

Abstract: A method for continuously tracking business performance impact of order sourcing systems and algorithms that decide how ecommerce orders should be fulfilled by assigning the items of the order to nodes in a fulfillment network such as stores, distribution centers, and third party logistics—to provide automatic root cause analysis and solution recommendations to pre-defined business problems arising from KPI monitoring. A Business Intelligence (BI) dashboard architecture operates with: 1) a monitoring module that continuously monitors business KPIs and creates abnormality alerts; and 2) a root cause analysis module that is designed specifically for each business problem to give real time diagnosis and solution recommendation. The root cause analysis module receives the created alert, and triggers conducting a root cause analysis at an analytics engine. The BI dashboard and user interface enables visualization of the KPI performance and root cause analysis results.

Abstract: Range query techniques are disclosed for use in accordance with data stream processing systems. A technique is provided for incrementally processing continual range queries against moving objects. This technique is applicable for location-aware services and applications. A technique for evaluating one or more continual range queries over one or more moving objects comprises maintaining a query index with one or more containment-encoded virtual constructs associated with the one or more continual range queries over the one or more moving objects, and incrementally evaluating the one or more continual range queries using the query index.

Abstract: A system, method and computer program product for continuously tracking business performance impact of order sourcing systems and algorithms that decide how ecommerce orders should be fulfilled by assigning the items of the order to nodes in a fulfillment network such as stores, distribution centers, and third party logistics—to provide automatic root cause analysis and solution recommendations to pre-defined business problems arising from KPI monitoring. A Business Intelligence (BI) dashboard architecture operates with: 1) a monitoring module that continuously monitors business KPIs and creates abnormality alerts; and 2) a root cause analysis module that is designed specifically for each business problem to give real time diagnosis and solution recommendation. The root cause analysis module receives the created alert, and triggers conducting a root cause analysis at an analytics engine. The BI dashboard and user interface enables visualization of the KPI performance and root cause analysis results.

Abstract: Range query techniques are disclosed for use in accordance with data stream processing systems. A technique is provided for incrementally processing continual range queries against moving objects. This technique is applicable for location-aware services and applications. A technique for evaluating one or more continual range queries over one or more moving objects comprises maintaining a query index with one or more containment-encoded virtual constructs associated with the one or more continual range queries over the one or more moving objects, and incrementally evaluating the one or more continual range queries using the query index.

Abstract: Range query techniques are disclosed for use in accordance with data stream processing systems. A technique is provided for incrementally processing continual range queries against moving objects. This technique is applicable for location-aware services and applications. A technique for evaluating one or more continual range queries over one or more moving objects comprises maintaining a query index with one or more containment-encoded virtual constructs associated with the one or more continual range queries over the one or more moving objects, and incrementally evaluating the one or more continual range queries using the query index.

Abstract: A method (and structure) for monitoring continual range queries against events includes decomposing each range query with one or more predefined virtual constructs, building a query index, and using the query index to match an event with the range queries.

Abstract: A system, method, and framework resulting therefrom, for a model-driven dashboard for business performance management, which includes capturing business dashboard model requirements at a business model level by providing at least one user-customizable model for capturing functionality of a dashboard, and after the user defines the functionality of the dashboard using the at least one user-customizable model, automatically generating code for a deployable dashboard application.

Abstract: An architecture, system, and method are provided for a commitment loop solution for on demand business activity management. A Business Performance Management (BPM) loop includes nodes each relating to different tasks for processing performance management trigger data generated by a business entity in real time. The beginning or sensing element of the loop is triggered by a token (representing a business event from a business process). The ending or executing element provides a business action to be implemented by the business entity. The token is passed from node to node sequentially in a particular order around the loop. Processing of the data represented by the token is governed by policies, which can be changed dynamically and follow a life cycle.

Abstract: Range query techniques are disclosed for use in accordance with data stream processing systems. A technique is provided for incrementally processing continual range queries against moving objects. This may be done for location-aware services and applications. For example, a technique for evaluating one or more continual range queries over one or more moving objects comprises maintaining a query index with one or more containment-encoded virtual constructs associated with the one or more continual range queries over the one or more moving objects, and incrementally evaluating the one or more continual range queries using the query index.

Abstract: Range query techniques are disclosed for use in accordance with data stream processing systems. In one aspect of the invention, a technique is provided for indexing continual range queries for use in data stream processing. For example, a technique for use in processing a data stream comprises obtaining at least one range query to be associated with the data stream, and building a range query index based on the at least one range query using one or more virtual constructs such that the query index is adaptive to one or more changes in a distribution of range query sizes. The step/operation of building the range query index may further comprise building the range query index such that the range query index accommodates one or more changes in query positions outside a monitoring area of the at least one range query. In another aspect of the invention, a technique is provided for incrementally processing continual range queries against moving objects.

Abstract: An architecture, system, and method are provided for a commitment loop solution for on demand business activity management. A Business Performance Management (BPM) loop is defined as a set of nodes each relating to different tasks for processing performance management trigger data generated by a business entity in real time, where the beginning or sensing element of the loop is triggered by a token (representing a business event from a business process) and the ending or executing element provides a business action to be implemented by the business entity. The token is passed from node to node sequentially in a particular order around the loop. Processing of the data represented by the token is governed by policies, which can be changed dynamically and follow a life cycle. On demand business activity management of larger aggregates of business entities (i.e.

Abstract: Deploying artifacts and tools in cloud computing environment for provisioning packaged software applications generates a user interface code automatically and dynamically using information from at least structural data representations of cloud computing environment and base configuration requirements for running one or more packaged software applications in the cloud computing environment. In one aspect, the automatically generated code may be augmented using one or more library codes and auxiliary files. Specific configuration requirements are further obtained by running the automatically generated user interface code. One or more components to deploy in the cloud computing environment are determined that meet the base configuration requirements and the specific configuration requirements, by analyzing the structural data representations.

Abstract: An architecture, system, and method are provided for a commitment loop solution for on demand business activity management. A Business Performance Management (BPM) loop includes nodes each relating to different tasks for processing performance management trigger data generated by a business entity in real time. The beginning or sensing element of the loop is triggered by a token (representing a business event from a business process). The ending or executing element provides a business action to be implemented by the business entity. The token is passed from node to node sequentially in a particular order around the loop. Processing of the data represented by the token is governed by policies, which can be changed dynamically and follow a life cycle.

Abstract: A mapping language, insertable into a DTD, allows automatic mapping from data sources into XML. A mapping results from the establishment of a correspondence between entities in a data source on the one hand and lists and scalars on the other hand. The language maps the lists and scalars to XML elements and attributes. The mapping language includes two constructs: the binding specification and the value specification. The value specification associates with a value or choice declaration. The binding specification includes at least one variable/expression pair. The constructs are insertable into a DTD to create an annotated DTD.

Abstract: Deploying artifacts and tools in cloud computing environment for provisioning packaged software applications generates a user interface code automatically and dynamically using information from at least structural data representations of cloud computing environment and base configuration requirements for running one or more packaged software applications in the cloud computing environment. In one aspect, the automatically generated code may be augmented using one or more library codes and auxiliary files. Specific configuration requirements are further obtained by running the automatically generated user interface code. One or more components to deploy in the cloud computing environment are determined that meet the base configuration requirements and the specific configuration requirements, by analyzing the structural data representations.

Abstract: Techniques are provided for performing structural joins for answering containment queries. Such inventive techniques may be used to perform efficient structural joins of two interval lists which are neither sorted nor pre-indexed. For example, in an illustrative aspect of the invention, a technique for performing structural joins of two element sets of a tree-structured document, wherein one of the two element sets is an ancestor element set and the other of the two element sets is a descendant element set, and further wherein each element is represented as an interval representing a start position and an end position of the element in the document, comprises the following steps/operations. An index is dynamically built for the ancestor element set. Then, one or more structural joins are performed by searching the index with the interval start position of each element in the descendant element set.

Abstract: Systems and methods for the identification of correlated burst events among two or more data streams, given one or more specific query time spans are disclosed. Also broadly contemplated is the act of finding, from one or more data streams, those streams that have correlated burst events with another given data stream within a time span.

Abstract: Systems and methods for the identification of correlated burst events among two or more data streams, given one or more specific query time spans are disclosed. Also broadly contemplated is the act of finding, from one or more data streams, those streams that have correlated burst events with another given data stream within a time span.