7 research outputs found
Direct Manipulation Querying of Database Systems.
Database systems are tremendously powerful and useful, as evidenced by their popularity in modern business. Unfortunately, for non-expert users, to use a database is still a daunting task due to its poor usability.
This PhD dissertation examines stages in the information seeking process and proposes techniques to help users interact with the database through direct manipulation, which has been proven a natural interaction paradigm. For the first stage of information seeking, query formulation, we proposed a spreadsheet algebra upon which a direct manipulation interface for database querying can be built. We developed a spreadsheet algebra that is powerful (capable of expressing at least all single-block SQL queries) and can be intuitively implemented in a spreadsheet. In addition, we proposed assisted querying by browsing, where we help users query the database through browsing. For the second stage, result review, instead of asking users to review possibly many results in a flat table, we proposed a hierarchical navigation scheme that allows users to browse the results through representatives with easy drill-down and filtering capabilities. We proposed an efficient tree-based method for generating the representatives. For the query refinement stage, we proposed and implemented a provenance-based automatic refinement framework. Users label a set of output tuples and our framework produces a ranked list of changes that best improve the query. This dissertation significantly lowers the barrier for non-expert users and reduces the effort for expert users to use a database.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86282/1/binliu_1.pd
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Analyzing, Mining, and Predicting Networked Behaviors
Network structure exists in various types of data in the real world, such as online and offline social networks, traffic networks, computer networks, brain networks, and countless other cases where there are relationships between different entities in the data. What are the roles of network structures in these data? First, the network captures inherent characteristics of the data themselves. This is clear from the definition of the network, which represents the relationship between entities: e.g., the social links among people in a social network describe how they interact with each other; a road network summarizes how the roads are laid out geographically; a brain network obtained from fMRI images represents pairs of brain regions that are active at the same time; a computer network constrains the paths via which internet packages and thus information or viruses can spread. Second, the network structures affect the evolution of the data over time. For example, new friendship links in an online social network are frequently created between friends of friends. Similarly, the current road network structure is without a doubt taken into consideration when roads are added or temporarily closed. As we grow, our brains also grow, including the additions of useful links or the clean up of unnecessary links between brain regions. Third, the network structures act as guidance for many different processes happening in the data. For instance, the links between users on social network dictate how gossips can spread; the roads influence how traffic flows in a city; the links between brain regions affects the way we think and how effectively we do things; the connections between computers route the transfer of any information on the internet.In this thesis, I studied the network effect in various networked behaviors, including analyzing such effect, finding its patterns, and predicting future networked behaviors. First, I gained insights into the data by analyzing the accompanied network structures as well as its evolution. Second, I proposed algorithms for mining different network patterns that help summarize the effect of the network structures on different networked behaviors. Finally, I proposed models to predict the evolution of networked behaviors over time. Toward these tasks, I explored a wide variety of network data, including protein-protein interaction networks, online social networks, collaboration networks, chemical compounds, and traffic networks. Overall, I tackled these network data in different aspects and developed a number of methods for effectively mining and forecasting networked behaviors in data