Interviewing During a Tight Job Market

Various tips for interviewing for PhD graduates, seeking an academic position in a research university in Asia or North America are discussed. It is suggested that having the dissertation done before interviews gives a large degree of relief on one\u27s mind. It is found that to be practical about job research package and keep a close eye on applications increases the confidence level. It is also observed that the questions during the talk provides opportunity to clarify and strengthen the talk and show this ability during the interview

Integrating Ontologies and Relational Data

In recent years, an increasing number of scientific and other domains have attempted to standardize their terminology and provide reasoning capabilities through ontologies, in order to facilitate data exchange. This has spurred research into Web-based languages, formalisms, and especially query systems based on ontologies. Yet we argue that DBMS techniques can be extended to provide many of the same capabilities, with benefits in scalability and performance. We present OWLDB, a lightweight and extensible approach for the integration of relational databases and description logic based ontologies. One of the key differences between relational databases and ontologies is the high degree of implicit information contained in ontologies. OWLDB integrates the two schemes by codifying ontologies\u27 implicit information using a set of sound and complete inference rules for SHOIN (the description logic behind OWL ontologies. These inference rules can be translated into queries on a relational DBMS instance, and the query results (representing inferences) can be added back to this database. Subsequently, database applications can make direct use of this inferred, previously implicit knowledge, e.g., in the annotation of biomedical databases. As our experimental comparison to a native description logic reasoner and a triple store shows, OWLDB provides significantly greater scalability and query capabilities, without sacrifcing performance with respect to inference

Quantifying Eavesdropping Vulnerability in Sensor Networks

With respect to security, sensor networks have a number of considerations that separate them from traditional distributed systems. First, sensor devices are typically vulnerable to physical compromise. Second, they have significant power and processing constraints. Third, the most critical security issue is protecting the (statistically derived) aggregate output of the system, even if individual nodes may be compromised. We suggest that these considerations merit a rethinking of traditional security techniques: rather than depending on the resilience of cryptographic techniques, in this paper we develop new techniques to tolerate compromised nodes and to even mislead an adversary. We present our initial work on probabilistically quantifying the security of sensor network protocols, with respect to sensor data distributions and network topologies. Beginning with a taxonomy of attacks based on an adversary’s goals, we focus on how to evaluate the vulnerability of sensor network protocols to eavesdropping. Different topologies and aggregation functions provide different probabilistic guarantees about system security, and make different trade-offs in power and accuracy

REX: Recursive, Delta-Based Data-Centric Computation

In today's Web and social network environments, query workloads include ad hoc and OLAP queries, as well as iterative algorithms that analyze data relationships (e.g., link analysis, clustering, learning). Modern DBMSs support ad hoc and OLAP queries, but most are not robust enough to scale to large clusters. Conversely, "cloud" platforms like MapReduce execute chains of batch tasks across clusters in a fault tolerant way, but have too much overhead to support ad hoc queries. Moreover, both classes of platform incur significant overhead in executing iterative data analysis algorithms. Most such iterative algorithms repeatedly refine portions of their answers, until some convergence criterion is reached. However, general cloud platforms typically must reprocess all data in each step. DBMSs that support recursive SQL are more efficient in that they propagate only the changes in each step -- but they still accumulate each iteration's state, even if it is no longer useful. User-defined functions are also typically harder to write for DBMSs than for cloud platforms. We seek to unify the strengths of both styles of platforms, with a focus on supporting iterative computations in which changes, in the form of deltas, are propagated from iteration to iteration, and state is efficiently updated in an extensible way. We present a programming model oriented around deltas, describe how we execute and optimize such programs in our REX runtime system, and validate that our platform also handles failures gracefully. We experimentally validate our techniques, and show speedups over the competing methods ranging from 2.5 to nearly 100 times.Comment: VLDB201

Piazza: Data Management Infrastructure for Semantic Web Applications

The Semantic Web envisions a World Wide Web in which data is described with rich semantics and applications can pose complex queries. To this point, researchers have defined new languages for specifying meanings for concepts and developed techniques for reasoning about them, using RDF as the data model. To flourish, the Semantic Web needs to be able to accommodate the huge amounts of existing data and the applications operating on them. To achieve this, we are faced with two problems. First, most of the world\u27s data is available not in RDF but in XML; XML and the applications consuming it rely not only on the domain structure of the data, but also on its document structure. Hence, to provide interoperability between such sources, we must map between both their domain structures and their document structures. Second, data management practitioners often prefer to exchange data through local point-to-point data translations, rather than mapping to common mediated schemas or ontologies. This paper describes the Piazza system, which addresses these challenges. Piazza offers a language for mediating between data sources on the Semantic Web, which maps both the domain structure and document structure. Piazza also enables interoperation of XML data with RDF data that is accompanied by rich OWL ontologies. Mappings in Piazza are provided at a local scale between small sets of nodes, and our query answering algorithm is able to chain sets mappings together to obtain relevant data from across the Piazza network. We also describe an implemented scenario in Piazza and the lessons we learned from it

MOSAIC: Unified Platform for Dynamic Overlay Selection and Composition

MOSAIC constructs new overlay networks with desired characteristics by composing existing overlays with subsets of those attributes. Thus, MOSAIC overcomes the problem of multiple network infrastructures that are partial solutions, while preserving deployability. Composition of control and/or data planes is possible in the system. MOSAIC overlays are specified in Mozlog, a declarative language that specifies overlay properties without binding them to a particular implementation or underlying network. This paper focuses on the runtime aspects of MOSAIC: how it enables interoperability between different overlay networks and how it implements switching between different overlay compositions, permitting dynamic compositions with both existing overlay networks and legacy applications. The system is validated experimentally using declarative overlay compositions concisely specified in Mozlog: an indirection overlay that supports mobility (i3), a resilient overlay (RON), and scalable lookups (Chord), all of which are combined to provide new functionality. MOSAIC provides the benefits of runtime composition to simultaneously deliver application-aware mobility, NAT traversal and reliability with low performance overhead, demonstrated by measurements on both a local cluster and PlanetLab

TAP: Time-Aware Provenance for Distributed Systems

In this paper, we explore the use of provenance for analyzing execution dynamics in distributed systems. We argue that provenance could have significant practical benefits for system administrators, e.g., for reasoning about changes in a system’s state, diagnosing protocol misconfigurations, detecting intrusions, and pinpointing performance bottlenecks. However, to realize this vision, we must revisit several aspects of provenance management. As a first step, we present time-aware provenance (TAP), an enhanced provenance model that explicitly represents time, distributed state, and state changes. We outline our research agenda towards developing novel query processing, languages, and optimization techniques that can be used to efficiently and securely query time-aware provenance, even in the presence of transient state or untrusted nodes

Sensor Network Security: More Interesting Than You Think

With the advent of low-power wireless sensor networks, a wealth of new applications at the interface of the real and digital worlds is emerging. A distributed computing platform that can measure properties of the real world, formulate intelligent inferences, and instrument responses, requires strong foundations in distributed computing, artificial intelligence, databases, control theory, and security. Before these intelligent systems can be deployed in critical infrastructures such as emergency rooms and powerplants, the security properties of sensors must be fully understood. Existing wisdom has been to apply the traditional security models and techniques to sensor networks. However, sensor networks are not traditional computing devices, and as a result, existing security models and methods are ill suited. In this position paper, we take the first steps towards producing a comprehensive security model that is tailored for sensor networks. Incorporating work from Internet security, ubiquitous computing, and distributed systems, we outline security properties that must be considered when designing a secure sensor network. We propose challenges for sensor networks – security obstacles that, when overcome, will move us closer to decreasing the divide between computers and the physical world

NetTrails: A Declarative Platform for Maintaining and Querying Provenance in Distributed Systems

We demonstrate NetTrails, a declarative platform for maintaining and interactively querying network provenance in a distributed system. Network provenance describes the history and derivations of network state that result from the execution of a distributed protocol. It has broad applicability in the management, diagnosis, and security analysis of networks. Our demonstration shows the use of NetTrails for maintaining and querying network provenance in a variety of distributed settings, ranging from declarative networks to unmodified legacy distributed systems. We conclude our demonstration with a discussion of our ongoing research on enhancing the query language and security guarantees