Add to ORKG87 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.In conclusion, the multi-dimensional analysis framework could lead to intuitive and insightful knowledge discovery on graphs, especially when the data is large and complex. Given the emerging trend of huge information networks as listed above, it is an important research topic to devote more efforts to. We point out a few possible future works, especially discovery-driven Graph OLAP. We believe that this is an interesting direction to go, and give our initial thoughts on it.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD
Analytics over big graphs is becoming a first-class challenge in database research, with fast-growin...
Abstract—In a wide array of disciplines, data can be modeled as an interconnected network of entitie...
Making sense of large graph datasets is a fundamental and challenging process that advances science,...
87 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.In conclusion, the multi-dimen...
Databases and data warehouse systems have been evolving from handling normalized spreadsheets stored...
Knowledge discovery is the process of discovering useful and previously unknown knowledge by analyzi...
Knowledge discovery is the process of discovering useful and previously unknown knowledge by analyzi...
157 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.The developed concepts, theor...
157 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.The developed concepts, theor...
Graph mining is the study of how to perform data mining and machine learning on data represented wit...
Abstract. The past few years have seen a tremendous increase in often irregularly structured data th...
Human exploration of large data sets becomes increasingly difficult with growing amounts of data. Fo...
Human exploration of large data sets becomes increasingly difficult with growing amounts of data. Fo...
Graphs naturally represent information in a wide range of disciplines, from social science to biolog...
Graphs naturally represent information in a wide range of disciplines, from social science to biolog...
Analytics over big graphs is becoming a first-class challenge in database research, with fast-growin...
Abstract—In a wide array of disciplines, data can be modeled as an interconnected network of entitie...
Making sense of large graph datasets is a fundamental and challenging process that advances science,...
87 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.In conclusion, the multi-dimen...
Databases and data warehouse systems have been evolving from handling normalized spreadsheets stored...
Knowledge discovery is the process of discovering useful and previously unknown knowledge by analyzi...
Knowledge discovery is the process of discovering useful and previously unknown knowledge by analyzi...
157 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.The developed concepts, theor...
157 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.The developed concepts, theor...
Graph mining is the study of how to perform data mining and machine learning on data represented wit...
Abstract. The past few years have seen a tremendous increase in often irregularly structured data th...
Human exploration of large data sets becomes increasingly difficult with growing amounts of data. Fo...
Human exploration of large data sets becomes increasingly difficult with growing amounts of data. Fo...
Graphs naturally represent information in a wide range of disciplines, from social science to biolog...
Graphs naturally represent information in a wide range of disciplines, from social science to biolog...
Analytics over big graphs is becoming a first-class challenge in database research, with fast-growin...
Abstract—In a wide array of disciplines, data can be modeled as an interconnected network of entitie...
Making sense of large graph datasets is a fundamental and challenging process that advances science,...