Encoding databases satisfying a given set of dependencies

Consider a relation schema with a set of dependency constraints. A fundamental question is what is the minimum space where the possible instances of the schema can be "stored". We study the following model. Encode the instances by giving a function which maps the set of possible instances into the set of words of a given length over the binary alphabet in a decodable way. The problem is to find the minimum length needed. This minimum is called the information content of the database. We investigate several cases where the set of dependency constraints consist of relatively simple sets of functional or multivalued dependencies. We also consider the following natural extension. Is it possible to encode the instances such a way that small changes in the instance cause a small change in the code. © 2012 Springer-Verlag

Datalog extension for nested relations

AbstractThe nested relational model allows relations that are not in first normal form. This paper gives an extension of Datalog rules for nested relations. In our approach, nested Datalog is a natural extension of Datalog introduced for the relational data model. A nested Datalog program has a hierarchical structure of rules and subprograms to manipulate relation values of nested relations. We introduce a new category of predicate symbols, the variable predicate symbols to refer to tuples of subrelations. The notion of soundness, safety and consistency is defined to avoid undesirable nested Datalog programs. The evaluation of nested Datalog is given in terms of the nested relational algebra. Finally, we relate the expressive power of nonrecursive nested Datalog to the power of nested relational algebra and safe nested tuple relational calculus

Temporal walk based centrality metric for graph streams

Abstract A plethora of centrality measures or rankings have been proposed to account for the importance of the nodes of a network. In the seminal study of Boldi and Vigna (2014), the comparative evaluation of centrality measures was termed a difficult, arduous task. In networks with fast dynamics, such as the Twitter mention or retweet graphs, predicting emerging centrality is even more challenging. Our main result is a new, temporal walk based dynamic centrality measure that models temporal information propagation by considering the order of edge creation. Dynamic centrality measures have already started to emerge in publications; however, their empirical evaluation is limited. One of our main contributions is creating a quantitative experiment to assess temporal centrality metrics. In this experiment, our new measure outperforms graph snapshot based static and other recently proposed dynamic centrality measures in assigning the highest time-aware centrality to the actually relevant nodes of the network. Additional experiments over different data sets show that our method perform well for detecting concept drift in the process that generates the graphs

ethp2psim: Evaluating and deploying privacy-enhanced peer-to-peer routing protocols for the Ethereum network

Network-level privacy is the Achilles heel of financial privacy in cryptocurrencies. Financial privacy amounts to achieving and maintaining blockchain- and network-level privacy. Blockchain-level privacy recently received substantial attention. Specifically, several privacy-enhancing technologies were proposed and deployed to enhance blockchain-level privacy. On the other hand, network-level privacy, i.e., privacy on the peer-to-peer layer, has seen far less attention and development. In this work, we aim to provide a peer-to-peer network simulator, ethp2psim, that allows researchers to evaluate the privacy guarantees of privacy-enhanced broadcast and message routing algorithms. Our goal is two-fold. First, we want to enable researchers to implement their proposed protocols in our modular simulator framework. Second, our simulator allows researchers to evaluate the privacy guarantees of privacy-enhanced routing algorithms. Finally, ethp2psim can help choose the right protocol parameters for efficient, robust, and private deployment