101,637 research outputs found
QueRIE: Collaborative Database Exploration
Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user’s querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user’s session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach
Image mining: trends and developments
[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining
Self-adaptive Scouting---Autonomous Experimentation for Systems Biology
We introduce a new algorithm for autonomous experimentation. This algorithm uses evolution to drive exploration during scientific discovery. Population size and mutation strength are self-adaptive. The only variables remaining to be set are the limits and maximum resolution of the parameters in the experiment. In practice, these are determined by instrumentation. Aside from conducting physical experiments, the algorithm is a valuable tool for investigating simulation models of biological systems. We illustrate the operation of the algorithm on a model of HIV-immune system interaction. Finally, the difference between scouting and optimization is discussed
Fast Cell Discovery in mm-wave 5G Networks with Context Information
The exploitation of mm-wave bands is one of the key-enabler for 5G mobile
radio networks. However, the introduction of mm-wave technologies in cellular
networks is not straightforward due to harsh propagation conditions that limit
the mm-wave access availability. Mm-wave technologies require high-gain antenna
systems to compensate for high path loss and limited power. As a consequence,
directional transmissions must be used for cell discovery and synchronization
processes: this can lead to a non-negligible access delay caused by the
exploration of the cell area with multiple transmissions along different
directions.
The integration of mm-wave technologies and conventional wireless access
networks with the objective of speeding up the cell search process requires new
5G network architectural solutions. Such architectures introduce a functional
split between C-plane and U-plane, thereby guaranteeing the availability of a
reliable signaling channel through conventional wireless technologies that
provides the opportunity to collect useful context information from the network
edge.
In this article, we leverage the context information related to user
positions to improve the directional cell discovery process. We investigate
fundamental trade-offs of this process and the effects of the context
information accuracy on the overall system performance. We also cope with
obstacle obstructions in the cell area and propose an approach based on a
geo-located context database where information gathered over time is stored to
guide future searches. Analytic models and numerical results are provided to
validate proposed strategies.Comment: 14 pages, submitted to IEEE Transaction on Mobile Computin
Designing algorithms to aid discovery by chemical robots
Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery
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