Transaction time databases retain and provide access to prior states of a database. An update "inserts" a new record while preserving the old version. Immortal DB builds transaction time database support into a database engine, not in middleware. It supports as of queries returning records current at the specified time. It also supports snapshot isolation concurrency control. Versions are stamped with the "clock times" of their updating transactions. The timestamp order agrees with transaction serialization order. Lazy timestamping propagates timestamps to transaction updates after commit. Versions are kept in an integrated storage structure, with historical versions initially stored with current data. Time-splits of pages permit large histories to be maintained, and enable time based indexing, which is essential for high performance historical queries. Experiments show that Immortal DB introduces little overhead for accessing recent database states while providing access to past states.

Immortal DB builds transaction time database support into the SQL Server engine, not in middleware. Transaction time databases retain and provide access to prior states of a database. An update "inserts" a new record while preserving the old version. The system supports as of queries returning records current at the specified time. It also supports snapshot isolation concurrency control. Versions are stamped with the times of their updating transactions. The timestamp order agrees with transaction serialization order. Lazy timestamping propagates timestamps to all updates of a transaction after commit. All versions are kept in an integrated storage structure, with historical versions initially stored with current data. Time-splits of pages permit large histories to be maintained, and enable time based indexing. We demonstrate Immortal DB with a moving objects application that tracks cars in the Seattle area.

Financial time series streams are watched closely by millions of traders. What exactly do they look for and how can we help them do it faster? Physicists study the time series emerging from their sensors. The same question holds for them. Musicians produce time series. Consumers may want to compare them. This tutorial presents techniques and case studies for four problems:1. Finding sliding window correlations in financial, physical, and other applications.2. Discovering bursts in large sensor data of gamma rays.3. Matching hums to recorded music, even when people don't hum well.4. Maintaining and manipulating time-ordered data in a database setting.This tutorial draws mostly from the book High Performance Discovery in Time Series: techniques and case studies, Springer-Verlag 2004. You can find the power point slides for this tutorial at http://cs.nyu.edu/cs/faculty/shasha/papers/sigmod04.ppt.The tutorial is aimed at researchers in streams, data mining, and scientific computing. Its applications should interest anyone who works with scientists or financial "quants." The emphasis will be on recent results and open problems. This is a ripe area for further advance.

Burst detection is the activity of finding abnormal aggregates in data streams. Such aggregates are based on sliding windows over data streams. In some applications, we want to monitor many sliding window sizes simultaneously and to report those windows with aggregates significantly different from other periods. We will present a general data structure for detecting interesting aggregates over such elastic windows in near linear time. We present applications of the algorithm for detecting Gamma Ray Bursts in large-scale astrophysical data. Detection of periods with high volumes of trading activities and high stock price volatility is also demonstrated using real time Trade and Quote (TAQ) data from the New York Stock Exchange (NYSE). Our algorithm beats the direct computation approach by several orders of magnitude.

IP network operators collect aggregate traffic statistics on network interfaces via the Simple Network Management Protocol (SNMP). This is part of routine network operations for most ISPs; it involves a large infrastructure with multiple network management stations polling information from all the network elements and collating a real time data feed. This demo will present a tool that manages the live SNMP data feed on a fully operational large ISP at industry scale. The tool primarily serves to study correlations in the network traffic, by providing a rich mix of ad-hoc querying based on a user-friendly correlation interface and as well as canned queries, based on the expertise of the network operators with field experience. The tool is called IPSOFACTO for IP Stream-Oriented FAst Correlation TOol.

A Query by Humming system allows the user to find a song by humming part of the tune. No musical training is needed. Previous query by humming systems have not provided satisfactory results for various reasons. Some systems have low retrieval precision because they rely on melodic contour information from the hum tune, which in turn relies on the error-prone note segmentation process. Some systems yield better precision when matching the melody directly from audio, but they are slow because of their extensive use of Dynamic Time Warping (DTW). Our approach improves both the retrieval precision and speed compared to previous approaches. We treat music as a time series and exploit and improve well-developed techniques from time series databases to index the music for fast similarity queries. We improve on existing DTW indexes technique by introducing the concept of envelope transforms, which gives a general guideline for extending existing dimensionality reduction methods to DTW indexes. The net result is high scalability. We confirm our claims through extensive experiments.

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An indirect adaptive control approach is developed in this paper for unknown robots using neural networks (NN). A key property of the proposed approach is that the actual joint angle values in the control law is replaced by the desired joint angle, angular speed and acceleration, and the bound on the NN reconstruction error is assumed to be unknown. Main theoretical results for designing such a neuro-adaptive controller are given, and the control performance of the proposed controller is verified with simulation studies.