Increasingly pervasive networks are leading towards a world where data is constantly in motion. In such a world, conventional techniques for query processing, which were developed under the assumption of a far more static and predictable computational environment, will not be sufficient. Instead, query processors based on adaptive dataflow will be necessary. The Telegraph project has developed a suite of novel technologies for continuously adaptive query processing. The next generation Telegraph system, called TelegraphCQ, is focused on meeting the challenges that arise in handling large streams of continuous queries over high-volume, highly-variable data streams. In this paper, we describe the system architecture and its underlying technology, and report on our ongoing implementation effort, which leverages the PostgreSQL open source code base. We also discuss open issues and our research agenda.

Recent work on querying data streams has focused on systems where newly arriving data is processed and continuously streamed to the user in real-time. In many emerging applications, however, ad hoc queries and/or intermittent connectivity also require the processing of data that arrives prior to query submission or during a period of disconnection. For such applications, we have developed PSoup, a system that combines the processing of ad-hoc and continuous queries by treating data and queries symmetrically, allowing new queries to be applied to old data and new data to be applied to old queries. PSoup also supports intermittent connectivity by separating the computation of query results from the delivery of those results. PSoup builds on adaptive query processing techniques developed in the Telegraph project at UC Berkeley. In this paper, we describe PSoup and present experiments that demonstrate the effectiveness of our approach.

Advances in data acquisition and sensor technologies are leading towards the development of “high fan-in ” architectures: widely distributed systems whose edges consist of numerous receptors such as sensor networks, RFID readers, or probes, and whose interior nodes are traditional host computers organized using the principles of cascading streams and successive aggregation. Examples include RFID-enabled supply chain management, largescale environmental monitoring, and various types of network and computing infrastructure monitoring. In this paper, we identify the key characteristics and data management challenges presented by high fan-in systems, and argue for a uniform, query-based approach towards addressing them. We then present our initial design concepts behind HiFi, the system we are building to embody these ideas, and describe a proof-of-concept prototype. 1.

Database-like query processing over a network of sensors has become an attractive paradigm for building sensor applications. A sensor query is characterized by data streams among participating sensor nodes with possible in-node data filtering or aggregation, and can be described as a tree-like data delivery pattern. The data delivery pattern can also be considered as a query execution plan, or query routing tree. In this work, we propose an algebraic optimization of the query routing tree construction and reconfiguration. In particular, we aim at generating query trees that maximize collision-free concurrent data transmissions hence reducing energy and time wastes due to retransmissions. Towards this, we introduce a Data Transmission Algebra (DTA) and apply it for efficient generation of such query trees. 1.

Sensor networks are being widely deployed for measurement, detection and surveillance applications. In these new applications, users issue long-running queries over a combination of stored data and sensor data. Most existing applications rely on a centralized system for collecting sensor data.