Data flows are a popular abstraction to define dataintensive processing tasks. In order to support a wide range of use cases, many data processing systems feature MapReduce-style user-defined functions (UDFs). In contrast to UDFs as known from relational DBMS, MapReduce-style UDFs have less strict templates. These templates do not alone provide all the information needed to decide whether they can be reordered with relational operators and other UDFs. However, it is well-known that reordering operators such as filters, joins, and aggregations can yield runtime improvements by orders of magnitude. We demonstrate an optimizer for data flows that is able to reorder operators with MapReduce-style UDFs written in an imperative language. Our approach leverages static code analysis to extract information from UDFs which is used to reason about the reorderbility of UDF operators. This information is sufficient to enumerate a large fraction of the search space covered by conventional RDBMS optimizers including filter and aggregation push-down, bushy join orders, and choice of physical execution strategies based on interesting properties. We demonstrate our optimizer and a job submission client that allows users to peek step-by-step into each phase of the optimization process: the static code analysis of UDFs, the enumeration of reordered candidate data flows, the generation of physical execution plans, and their parallel execution. For the demonstration, we provide a selection of relational and nonrelational data flow programs which highlight the salient features of our approach.

By means of so-called Scatternets, Bluetooth provides the ability to construct robust wireless multi-hop networks. In this paper we propose a practical protocol for time synchronization of such Bluetooth multi-hop networks. Our protocol makes use of the internal clock maintained by Bluetooth, requires minimal communication overhead, and provides an accuracy of few milliseconds across multiple hops. The protocol has been implemented and evaluated on BTnodes, an embedded computing platform which uses Bluetooth for ad hoc networking.

Large-scale deployment of sensor networks is more and more becoming an issue to researchers and industry alike. The recently revised BTnode architecture provides two wireless radios and facilitates the interconnection of heterogeneous devices. Apart from offering interesting new opportunities in using multi-frontend devices in sensor-network research, this architecture is optimally suited for deployment-support networks as introduced in the following.

We motivate a prototyping platform for ad hoc networking research showing some requirements and constraints. The architecture of the BTnodes, each of which can store information, compute and communicate, is explained in conjunction with some demo applications that have been implemented. Important requirements and design trade-offs to be able to support multiple, compatible communication interfaces, to handle limited resources, for power-aware operation and for efficient testbed deployment are discussed.