Rafael J Fernandez-Moctezuma

Techniques for Validating an Automatic Bottleneck Detection Tool Using Archived Freeway Sensor Data

Bottlenecks are key features of freeway systems. Their effects in performance and emissions are of increasing importance as congestion worsens in urban areas. In the United States, FHWA has been working to identify and monitor key bottlenecks in each state. In Oregon, a freeway data archive known as the Portland Oregon Regional Transportation Archive Listing archives measured count, density, and speed data from more than 600 locations at 20-s intervals. This archive has enabled development of online freeway performance and reliability analysis tools. This paper describes the rigorous evaluation and refinement of an automated tool for identifying recurrent freeway bottlenecks using historical data within the framework of the data archive. Efforts have focused on identification and display of active bottleneck features by using graphical tools and the selection of optimal variables that enabled careful identification of active bottlenecks. This research aims to detect bottleneck activation historically and, through future work, in real time as well. Ultimately, the results of this research will enhance the prioritization of improvements and implementation of operational strategies on the freeway network.

Toward Management and Imputation of Unavailable Data in Online Advanced Traveler Information Systems

Advanced traveler information systems (ATIS) deliver information derived from intelligent transportation systems data. ATIS continue to emerge and grow in popularity, as some of these systems are public and freely available. ATIS performance and perceived usefulness depends, among several design factors, on the reliability and quality of its data sources, especially when the systems are reporting current conditions. We present a sample scenario in which an inductive loop detector station along a freeway stops transmitting data, and an ATIS service presents an estimated state of the system instead of showing that no data is available. The estimation is based on sources of available and correlated data. Our first approach to implement an estimation framework consists of deriving a parametric description of the correlation from archived historical data. An example application is provided as well as suggestions for future research.

Framing the question: detecting and filling spatial-temporal windows

We propose a new mechanism, which we term frames, for data-dependent windows. In contrast to traditional timestamp-based windows, frames represent just the boundary of a window and can be filled with data from secondary streams or historical data. Examples show how frames can be useful in network and sensor monitoring applications. We present frame definition and implementation in one dimension, discuss extension to multidimensional frames, and identify issues for further investigation.

Towards execution guarantees for stream queries

The unbounded nature of data streams and the low-latency requirements of stream processing present interesting challenges in Data Stream Management System (DSMS) design. Streaming query operators are typically designed to produce results with low latency, as well as to efficiently manage their state. Stream-progress delimitation techniques, such as punctuation, can help query operators achieve these goals. In this work, we look at deriving execution guarantees with respect to result production and state management for complete queries over punctuated streams. These guarantees are derived before query execution. We formalize notions of successful stream processing at an operator level, and extend these definitions to stream queries as a whole. We introduce a framework, punctuation contracts, for analyzing data processing and punctuation propagation from input to output on individual operators. We then use our framework to analyze complete queries and determine, prior to execution, if every valid input is eventually emitted, and no item remains in operator state indefinitely. Finally, we discuss extensions needed to bound query memory requirements; we describe four stream properties that can be used to help understand and quantify memory and CPU usage.

Toward Formal Semantics for Data and Schema Evolution in Data Stream Management Systems

Data Stream Management Systems (DSMSs) do not statically respond to issued queries -- rather, they continuously produce result streams to standing queries, and often operate in a context where any interruption can lead to data loss. Support for schema evolution in continuous query processing is currently unaddressed. In this work we address evolution in DSMSs by proposing semantics for three evolution primitives: Add Attribute and Drop Attribute (schema evolution), and Alter Data (data evolution). We characterize how a subset of commonly used query operators in a DSMS act on and propagate these primitives.