Abstract: An educational institution (also referred as a university) is rich with multiple kinds of data: students, faculty members, departments, divisions, and at university level. Relating and correlating this data at and across various levels help in obtaining a perspective about the educational institution. A structural representation captures the essence of all of the relationships in a unified manner and an important aspect of the relationship is the so-called “influence factor.” This factor indicates influencing effect of an entity over another entity, wherein the entities are a part of the structural representation. Given such a structural representation, a system and method that propagates the influence factors of the entities to arrive at a stable representation from the point of view of influences is discussed.

Abstract: An educational institution (also referred as a university) is structurally modeled using a university model graph. A key benefit of modeling of the educational institution is to help in an introspective analysis by the educational institute. Specifically, the model is quite beneficial for undertaking the analysis of the various issues faced by the educational institute. A what-if scenario requires the model to be suitably changed to address the issue under consideration and the changed model needs to be analyzed to determine how the issue could be handled. A system and method for what-if scenario analysis based on the university model graph is discussed.

Abstract: An educational institution (also referred as a university) is structurally modeled using a university model graph. A key benefit of modeling of the educational institution is to help in an introspective analysis by the educational institute. The analysis of the various activities performed on the university campus by the various entities (such as students) of the university leads to the generation of student specific activity maps. These maps play a role in counseling students in organizing and planning of their activities in the context of the university. A system and method for automated generation of mirror maps involves the combined analysis of a set of activity maps of a set of students to measure their similarities. Such similarities help, for example, in the process of formation of teams, and identification of meeting times and venues.

Abstract: An educational institution (also referred as a university) is rich with multiple kinds of data: students, faculty members, departments, divisions, and at university level. A structural representation that captures the essence of all of the relationships in a unified manner has concise information about the educational institution, and visualization is a way to bring out all this information in an explicit manner so that the various of the users of the educational institution system understand effectively their system. A system and method for visualization based on the structural representation of a university along a variety of dimensions is discussed.

Abstract: An educational institution (also referred as a university) is rich with multiple kinds of data: students, faculty members, departments, divisions, and at university level. Relating and correlating this data at and across various levels help in obtaining a perspective about the educational institution. A structural representation captures the essence of all of the relationships in a unified manner and an important aspect of the relationship is the so-called “influence factor.” This factor indicates influencing effect of an entity over another entity, wherein the entities are a part of the structural representation. A system and method for the construction of such a structural representation of an educational institution based on the educational institution specific information is discussed.

Abstract: An educational institution (also referred as a university) is rich with multiple kinds of data: students, faculty members, departments, divisions, and at university level. Relating and correlating this data at and across various levels help in obtaining a perspective about the educational institution. A structural representation captures the essence of all of the relationships in a unified manner and an important aspect of the relationship is the so-called “influence factor.” This factor indicates influencing effect of an entity over another entity, wherein the entities are a part of the structural representation. Given such a structural representation, a system and method that propagates the influence factors of the entities to arrive at a stable representation from the point of view of influences is discussed.

Abstract: An educational institution (also referred as a university) is structurally modeled using a university model graph. Such a model helps compare educational institutions at various levels—university level, department level, faculty member level, or student level. One of the requirements of comparison is to normalize the similarities and identify and elaborate the differences across multiple educational institutions. A way to achieve this is to model the educational institutions using comparable elements; specifically, the university model graph allows for such comparison as multiple educational institutions are modeled based on the same set of concepts and notions. A system and method for comparing educational institutions based on their respective university model graphs is discussed.

Abstract: An educational institution (also referred as a university) is structurally modeled using a university model graph. A key benefit of modeling of the educational institution is to help in an introspective analysis by the educational institute. In order to build an effective university model graph, it is required to gather and analyze the various activities performed on the university campus by the various entities of the university. A system and method for automated generation of activity flows involves analysis of multiple student specific sub-activities and correlating them from temporal and spatial points of view. Specifically, the presented system allows for reliable identification of activity flows accounting for duplicate and missing sub-activities.

Abstract: An educational institution (also referred as a university) is structurally modeled using a university model graph. A key benefit of modeling of the educational institution is to help in an introspective analysis by the educational institute. In order to build an effective university model graph, it is required to gather and analyze the various activities performed on the university campus by the various entities of the university. A system and method for automated activity gathering that involves instrumented components, sub-systems, and networks is discussed. Specifically, the presented system allows for reliable identification of activities performed by a student of the university based on inputs received from multiple sources associated with the instrumented components, sub-systems, and networks.

Abstract: An educational institution (also referred as a university) is structurally modeled using a university model graph. Such a model helps compare educational institutions at various levels—university level, department level, faculty member level, or student level. One of the requirements of comparison is to normalize the similarities and identify and elaborate the differences across multiple educational institutions. A way to achieve this is to model the educational institutions using comparable elements; specifically, the university model graph allows for such comparison as multiple educational institutions are modeled based on the same set of concepts and notions. A system and method for comparing educational institutions based on their respective university model graphs is discussed.

Abstract: An educational institution (also referred as a university) is structurally modeled using a university model graph. A key benefit of modeling of the educational institution is to help in an introspective analysis by the educational institute. Specifically, the model is quite beneficial for undertaking the analysis of the various issues faced by the educational institute. A what-if scenario requires the model to be suitably changed to address the issue under consideration and the changed model needs to be analyzed to determine how the issue could be handled. A system and method for what-if scenario analysis based on the university model graph is discussed.

Abstract: An educational institution (also referred as a university) is rich with multiple kinds of data: students, faculty members, departments, divisions, and at university level. Relating and correlating this data at and across various levels help in obtaining a perspective about the educational institution. A structural representation captures the essence of all of the relationships in a unified manner and an important aspect of the relationship is the so-called “influence factor.” This factor indicates influencing effect of an entity over another entity, wherein the entities are a part of the structural representation. A system and method for the construction of such a structural representation of an educational institution based on the educational institution specific information is discussed.

Abstract: IP networks carry packets that consist of headers and payloads. Typical traffic analysis systems at layer 3 process packet headers in order to obtain as much information about the traffic as possible. However, performing of deep packet analysis requires the processing of packet payloads as well. Another important requirement of layer 3 processing is the need to process the payloads at wire speeds. A system and method for deep packet inspection at layer 3 involves (a) an approach for packet payload processing; (b) accounting for out of order arrival of packets; (c) an approach for partial match analysis so as to be able to analyze the traffic flows when only partial information is available; and (d) an approach for effective payload processing for attempting to achieve wire speed processing.

Abstract: An educational institution (also referred as a university) is rich with multiple kinds of data: students, faculty members, departments, divisions, and at university level. A structural representation that captures the essence of all of the relationships in a unified manner has concise information about the educational institution, and visualization is a way to bring out all this information in an explicit manner so that the various of the users of the educational institution system understand effectively their system. A system and method for visualization based on the structural representation of a university along a variety of dimensions is discussed.

Abstract: An educational institution (also referred as a university) is rich with multiple kinds of data: students, faculty members, departments, divisions, and at university level. Relating and correlating this data at and across various levels help in obtaining a perspective about the educational institution. A structural representation captures the essence of all of the relationships in a unified manner and an important aspect of the relationship is the so-called “influence factor.” This factor indicates influencing effect of an entity over another entity, wherein the entities are a part of the structural representation. Given such a structural representation, a system and method that propagates the influence factors of the entities to arrive at a stable representation from the point of view of influences is discussed.

Abstract: IP networks carry packets that consist of headers and payloads. Typical traffic analysis systems at layer 3 process packet headers in order to obtain as much information about the traffic as possible. However, performing of deep packet analysis requires the processing of packet payloads as well. Another important requirement of layer 3 processing is the need to process the payloads at wire speeds. A system and method for deep packet inspection at layer 3 involves (a) an approach for packet payload processing; (b) accounting for out of order arrival of packets; (c) an approach for partial match analysis so as to be able to analyze the traffic flows when only partial information is available; and (d) an approach for effective payload processing for attempting to achieve wire speed processing.