The present review is the first in an ongoing guide to medical statistics, using specific examples from intensive care. The first step in any analysis is to describe and summarize the data. As well as becoming familiar with the data, this is also an opportunity to look for unusually high or low values (outliers), to check the assumptions required for statistical tests, and to decide the best wa...

Der Prozess valide, neuartige, potenziell nutzbare und verständliche Muster in Daten zu finden, wird als Knowledge Discovery in Database Prozess bezeichnet (KDD-Prozess). Die diesem Prozess zu Grunde liegende Datenbasis unterliegt einem ständigen Wandel. Doug Laney erkannte die Eigenschaften Volume, Variety und Velocity als neue Herausforderungen für ITOrganisationen. Heute werden diese Herausf...

We present a set of time-efficient approaches to index objects moving on the plane to efficiently answer range queries about their future positions. Our algorithms are based on previously described solutions as well as on the employment of efficient data structures. Finally, an experimental evaluation is included that shows the performance, scalability and efficiency of our methods.

We describe a new algorithm for answering a given set of range queries under -differential privacy which often achieves substantially lower error than competing methods. Our algorithm satisfies differential privacy by adding noise that is adapted to the input data and to the given query set. We first privately learn a partitioning of the domain into buckets that suit the input data well. Then w...

In data-centric sensor networks, various data items, such as temperature, humidity, pressure and so on, are sensed and stored in sensor nodes. As these attributes are mostly scalar values and inter-related, multi-dimensional range queries are very useful. However, the previous work on range query processing in sensor networks did not consider overall network lifetime. To prolong network lifetim...

Database Lab, University of Massachusetts Sep 2014 Present Research Assistant, advised by Gerome Miklau · Work on privacy-preserving data analysis. Contribute to DPBench, an experimental infrastructure to facilitate study of differentially private algorithms. · Conduct comprehensive empirical study of 15 published algorithms for 1D and 2D linear range queries on a total of 27 datasets. Result i...

We consider the dominating point set reporting problem in two-dimension. We propose a data structure for finding the set of dominating points inside a given orthogonal query rectangle. Given a set of n points in the plane, it supports 4-sided queries in O(log n+k), where k is size of the output, using O(n log n) space. This work can be of application when range queries are generated using mobil...

In this paper we describe a dynamic external memory data structure that supports range reporting queries in three dimensions in O(logB N + k B ) I/O operations, where k is the number of points in the answer and B is the block size. This is the first dynamic data structure that answers three-dimensional range reporting queries in log O(1)

Probabilistic range query is an important type of query in the area of uncertain data management. A probabilistic range query returns all the objects within a specific range from the query object with a probability no less than a given threshold. In this paper we assume that each uncertain object stored in the databases is associated with a multi-dimensional Gaussian distribution, which describ...

We describe a new algorithm for answering a given set of range queries under -differential privacy which often achieves substantially lower error than competing methods. Our algorithm satisfies differential privacy by adding noise that is adapted to the input data and to the given query set. We first privately learn a partitioning of the domain into buckets that suit the input data well. Then w...