Many problems require working with data that varies in its structure and content. Current approaches, such as schema evolution or data integration tools, are highly tailored to specific kinds of variation in databases. While these approaches work well in their roles, they do not address all kinds of variation and do address the interaction of different kinds of variation in databases. In this paper, we define a framework for capturing variation as a generic and orthogonal con- cern in relational databases. We define variational schemas, variational databases, and variational queries for capturing variation in the structure, content, and information needs of relational databases, respectively. We define a type system that ensures variational queries are consistent with respect to a variational schema. Finally, we design and implement a variational database management system as an abstraction layer over a traditional relational database management system. Using previously developed use cases, we show the feasibility of our framework and demonstrate the performance of different approaches used in our system

Asymmetric tensor fields have found applications in many science and engineering domains, such as fluid dynamics. Recent advances in the visualization and analysis of 2D asymmetric tensor fields focus on pointwise analysis of the tensor field and effective visualization metaphors such as colors, glyphs, and hyperstreamlines. In this paper, we provide a novel multi-scale topological analysis framework for asymmetric tensor fields on surfaces. Our multi-scale framework is based on the notions of eigenvalue and eigenvector graphs. At the core of our framework are the identification of atomic operations that modify the graphs and the scale definition that guides the order in which the graphs are simplified to enable clarity and focus for the visualization of topological analysis on data of different sizes. We also provide efficient algorithms to realize these operations. Furthermore, we provide physical interpretation of these graphs. To demonstrate the utility of our system, we apply our multi-scale analysis to data in computational fluid dynamics.

Denial-of-service (DoS) attacks are considered within the province of a shared channel model in which attack rates may be large but are bounded and client request rates vary within fixed bounds. In this setting, it is shown that clients can adapt effectively to an attack by increasing their request rate based on timeout windows to estimate attack rates. The server will be able to process client requests with high probability while pruning out most of the attack by selective random sampling. The protocol introduced here, called Adaptive Selective Verification (ASV), is shown to use bandwidth efficiently and does not require any server state or assumptions about network congestion. The main results of the paper are a formulation of optimal performance and a proof that ASV is optimal.

Attribute-Based Messaging (ABM) enables messages to be addressed using attributes of recipients rather than an explicit list of recipients. Such messaging offers benefits of efficiency, exclusiveness, and intensionality, but faces challenges in access control and confidentiality. In this article we explore an approach to intraenterprise ABM based on providing access control and confidentiality using information from the same attribute database exploited by the addressing scheme. We show how to address three key challenges. First, we demonstrate a manageable access control system based on attributes. Second, we demonstrate use of attribute-based encryption to provide end-to-end confidentiality. Third, we show that such a system can be efficient enough to support ABM for mid-size enterprises. Our implementation can dispatch confidential ABM messages approved by XACML policy review for an enterprise of at least 60,000 users with only seconds of latency.

We consider Denial of Service (DoS) attacks within the province of a shared channel model in which attack rates may be large but are bounded and client request rates vary within fixed bounds. In this setting it is shown that the clients can respond effectively to an attack by using bandwidth as a payment scheme and time-out windows to adaptively boost request rates. The server will be able to process client requests with high probability while pruning out most of the attack by selective random sampling. Our protocol, which we call Adaptive Selective Verification (ASV) is shown to be efficient in terms of bandwidth consumption using both a theoretical model and network simulations. It differs from previously-investigated adaptive mechanisms for bandwidth-based payment by requiring very limited state on the server.

Attribute based messaging (ABM) enables message senders to dynamically create a list of recipients based on their attributes as inferred from an enterprise database. Such targeted messaging can reduce unnecessary communications and enhance privacy, but faces challenges in access control. In this paper, we explore an approach to ABM based on deriving access control information from the same attribute database exploited by the addressing scheme. We show how to address three key challenges. First, we demonstrate a manageable access control system based on attributes. Second we show how this can be used with existing messaging systems to provide a practical deployment strategy. Third, we show that such a system can be efficient enough to support ABM for mid-size enterprises. Our implementation can dispatch ABM messages approved by XACML review for an enterprise of at least 60,000 users with only seconds of latency