We present the first learned index that supports predecessor, range queries and updates
within provably efficient time and space bounds in the worst case. In the (static) context of …

Abstract Methods for Approximate Query Processing (AQP) are essential for dealing with
massive data. They are often the only means of providing interactive response times when …

Index structures in memory systems become important to improve the entire system
performance. The promising learned indexes leverage deep-learning models to …

As the volumes of sensor data being accumulated are likely to soar, data compression has
become essential in a wide range of sensor-data applications. This has led to a plethora of …

Given a time series data stream, the generation of error-bounded Piecewise Linear
Representation (error-bounded PLR) is to construct a number of consecutive line segments …

Data compression is crucial in the networks as there is limited energy which is accessible to
sensor nodes in wireless sensor networks (WSNs). The sensor nodes lifetime is extended …

Continuous “always-on” monitoring is beneficial for a number of applications, but potentially
imposes a high load in terms of communication, storage and power consumption when a …

We consider the problem of collectively approximating a set of sensor signals using the least
amount of space so that any individual signal can be efficiently reconstructed within a given …

Wireless Sensor Networks (WSNs) encounter a substantial challenge when it comes to
energy conservation. As sensor nodes rely on battery power to operate in unattended …

In this paper we investigate algorithms and lower bounds for summarization problems over a
single pass data stream. In particular we focus on histogram construction and K-center …