Abstract-Trust is an important yet complex and little understood dyadic relation among actors in a social network. There are many dimensions to trust; trust plays an important role in the formation of coalitions in social networks, in assessing quality and credibility of information as well as in determining how information flows through the network.In this paper, we present algorithmically quantifiable measures of trust which can be determined from the communication behavior of the actors in a social communication network. The basis for our study is a proposition that trust results in likely communication behavior patterns which are statistically different from random communication in a network. Detecting the statistically significant realizations of this trust-like behavior allows us to develop a quantitative measure of who-trusts-whom relation in the network.Since our measure of trust is based on quantifiable behavior, we call it behavioral trust. We develop algorithms to efficiently compute behavioral trust and we validate these measures on the Twitter network.

Distributed key-value stores are now a standard component of high-performance web services and cloud computing applications. While key-value stores offer significant performance and scalability advantages compared to traditional databases, they achieve these properties through a restricted API that limits object retrieval-an object can only be retrieved by the (primary and only) key under which it was inserted. This paper presents HyperDex, a novel distributed key-value store that provides a unique search primitive that enables queries on secondary attributes. The key insight behind HyperDex is the concept of hyperspace hashing in which objects with multiple attributes are mapped into a multidimensional hyperspace. This mapping leads to efficient implementations not only for retrieval by primary key, but also for partially-specified secondary attribute searches and range queries. A novel chaining protocol enables the system to achieve strong consistency, maintain availability and guarantee fault tolerance. An evaluation of the full system shows that HyperDex is 12-13× faster than Cassandra and MongoDB for finding partially specified objects. Additionally, HyperDex achieves 2-4× higher throughput for get/put operations.

Distributed key-value stores are now a standard component of high-performance web services and cloud computing applications. While key-value stores offer significant performance and scalability advantages compared to traditional databases, they achieve these properties through a restricted API that limits object retrieval-an object can only be retrieved by the (primary and only) key under which it was inserted. This paper presents HyperDex, a novel distributed key-value store that provides a unique search primitive that enables queries on secondary attributes. The key insight behind HyperDex is the concept of hyperspace hashing in which objects with multiple attributes are mapped into a multidimensional hyperspace. This mapping leads to efficient implementations not only for retrieval by primary key, but also for partially-specified secondary attribute searches and range queries. A novel chaining protocol enables the system to achieve strong consistency, maintain availability and guarantee fault tolerance. An evaluation of the full system shows that HyperDex is 12-13× faster than Cassandra and MongoDB for finding partially specified objects. Additionally, HyperDex achieves 2-4× higher throughput for get/put operations.

Graph databases have become a common infrastructure component. Yet existing systems either operate on offline snapshots, provide weak consistency guarantees, or use expensive concurrency control techniques that limit performance. In this paper, we introduce a new distributed graph database, called Weaver, which enables efficient, transactional graph analyses as well as strictly serializable ACID transactions on dynamic graphs. The key insight that allows Weaver to combine strict serializability with horizontal scalability and high performance is a novel request ordering mechanism called refinable timestamps. This technique couples coarse-grained vector timestamps with a fine-grained timeline oracle to pay the overhead of strong consistency only when needed. Experiments show that Weaver enables a Bitcoin blockchain explorer that is 8x faster than Blockchain.info, and achieves 10.9x higher throughput than the Titan graph database on social network workloads and 4x lower latency than GraphLab on offline graph traversal workloads.

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