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35 Papers

EmbedDB: A High-Performance Time Series Database for Embedded Systems

Topics: Performance Evaluation and Benchmarking; Non-Relational Databases; Internet of Things; Query Languages and Query Processing

Authors: Justin Schoenit , Seth Akins and Ramon Lawrence

Affiliation: University of British Columbia, Kelowna, BC, Canada

Digital Object Identifier: 10.5220/0012558100003690

Proceedings of ICEIS 2024, ISBN: 978-989-758-692-7

Quick Abstract

Efficient data processing on embedded devices may reduce network communication and improve battery usage allowing for longer sensor lifetime. Data processing is challenged by limited CPU and memory hardware. EmbedDB is a key-value data store supporting time series and relational data on memory-constrained devices. EmbedDB is competitive with SQLite on more powerful embedded hardware such as the Raspberry Pi and executes on hardware such as Arduinos that SQLite and other previous systems cannot. Experimental results evaluating EmbedDB on time series query processing show a speedup of five times compared to SQLite on a Raspberry Pi on many queries, and the ability to execute data processing on small embedded systems not well supported by existing databases.

A Data Analysis Pipeline for Automating Apple Trait Analysis and Prediction

Topics: Data Analytics; Data Science; Deep Learning; Data Fusion

Authors: Kyle Ranslam and Ramon Lawrence

Affiliation: University of British Columbia, Kelowna, BC, Canada

Digital Object Identifier: 10.5220/0012088300003541

Proceedings of DATA 2023, ISBN: 978-989-758-664-4

Quick Abstract

Feeding the world’s growing population requires research and development of fruit varieties that can be sustainably grown with high yields and quality and require low inputs of water and fertilizer. The process of developing new fruit varieties is data-intensive and traditionally uses manual processes that do not scale. The contribution of this work is a data analysis pipeline that automates the extraction of fruit characteristics from images and integrates multiple data sources (images, field measurements, human evaluation) to help direct the research to the most promising candidates and reduce the amount of manual time required for data collection and analysis. Initial results demonstrate that the image analysis is accurate and can be done at scale in a realworld environment.

Using Learned Indexes to Improve Time Series Indexing Performance on Embedded Sensor Devices

Topics: Internet of Things; Embedded Systems; Data Collection; Data Storage and Transmission

Authors: David Ding , Ivan Carvalho and Ramon Lawrence

Affiliation: University of British Columbia, Kelowna, BC, Canada

Digital Object Identifier: 10.5220/0011692900003399

Proceedings of SENSORNETS 2023, ISBN: 978-989-758-635-4

Quick Abstract

Efficiently querying data on embedded sensor and IoT devices is challenging given the very limited memory and CPU resources. With the increasing volumes of collected data, it is critical to process, filter, and manipulate data on the edge devices where it is collected to improve efficiency and reduce network transmissions. Existing embedded index structures do not adapt to the data distribution and characteristics. This paper demonstrates how applying learned indexes that develop space efficient summaries of the data can dramatically improve the query performance and predictability. Learned indexes based on linear approximations can reduce the query I/O by 50 to 90% and improve query throughput by a factor of 2 to 5, while only requiring a few kilobytes of RAM. Experimental results on a variety of time series data sets demonstrate the advantages of learned indexes that considerably improve over the state-of-the-art index algorithms.

Efficient Flash Indexing for Time Series Data on Memory-constrained Embedded Sensor Devices

Topics: Environment Monitoring; Embedded Systems; Data Collection; Data Storage and Transmission

Authors: Scott Fazackerley , Nadir Ould-Khessal and Ramon Lawrence

Affiliation: University of British Columbia, Kelowna, BC, Canada

Digital Object Identifier: 10.5220/0010318800920099

Proceedings of SENSORNETS 2021, ISBN: 978-989-758-489-3

Quick Abstract

Embedded sensor devices with limited hardware resources must efficiently collect environmental and industrial time series data for analysis. Performing data analysis on the device requires data storage and indexing that minimizes memory, I/O, and energy usage. This paper presents an index structure that is optimized for the constrained use cases associated with sensor time series collection and analysis. By supporting only planned queries and analysis patterns, the storage and indexing implementation is simplified, and outperforms general techniques based on hashing and trees. The indexing technique is analyzed and compared with other indexing approaches and is adapted to all flash memory types including memory that supports overwriting.

Practical Precision Agriculture with LoRa based Wireless Sensor Networks

Topic: Wireless Sensor Networks

Authors: Jonathan Gresl ¹ , Scott Fazackerley ² and Ramon Lawrence ¹

Affiliations: ¹ Department of Computer Science, University of British Columbia, Kelowna, Canada , ² Department of Electronic Engineering Technology, Okanagan College, Kelowna, Canada

Digital Object Identifier: 10.5220/0010394401310140

Proceedings of WSN4PA 2021, ISBN: 978-989-758-489-3

Quick Abstract

Precision agriculture is enabled by using real-time data to manage environmental variations, measure performance to improve upon previous seasons, and perform predictive analysis to make better growing decisions, resulting in higher production yields with lower production costs. Although beneficial, collecting and analyzing the environmental data is an expensive and complicated endeavor. Numerous existing wireless sensor network (WSN) solutions rely on protocols such as 802.15.4 b (LR-WPANs) and 802.11x (WLAN) but provide limited transmission range, complex communication stacks and data management, and high power consumption. Additionally, many existing services introduce challenges with data ownership and residency. These factors present a high barrier to entry for growers. The resources required to implement and maintain sensor networks are too high to justify the investment. This work presents an approach that uses inexpensive and effective hardware that is easily setup and maintained. Costs to implement the network are reduced through the use of open-source hardware. Transmission ranges and power consumption are improved by using long range (LoRa) radio transceivers. By addressing these limitations, growers will be better enabled to adopt new technologies, ultimately improving sustainability, viability, quality and profit margins in agriculture.

Adapting Linear Hashing for Flash Memory Resource-constrained Embedded Devices

Topics: Performance Evaluation and Benchmarking; Query Languages and Query Processing; Distributed Database Systems

Authors: Andrew Feltham , Spencer MacBeth , Scott Fazackerley and Ramon Lawrence

Affiliation: University of British Columbia, Kelowna and Canada

Digital Object Identifier: 10.5220/0007709301760181

Proceedings of ICEIS 2019, ISBN: 978-989-758-372-8

Quick Abstract

Linear hashing provides constant time operations for data indexing and has been widely implemented for database systems. Embedded devices, often with limited memory and CPU resources, are increasingly collecting and processing more data and benefit from fast index structures. Implementing linear hashing for flash-based embedded devices is challenging both due to the limited resources and the unique properties of flash memory. In this work, an implementation of linear hashing optimized for embedded devices is presented and evaluated. Experimental results demonstrate that the implementation has constant time performance on embedded devices, even with as little as 8 KB of memory, and offers benefits for several use cases.

Related Ontology Subjects/Areas/Topics (5)

Databases and Information Systems Integration		Enterprise Information Systems		Query Languages and Query Processing
Distributed Database Systems		Performance Evaluation and Benchmarking

Automatic Polyp Detection from Endoscope Image using Likelihood Map based on Edge Information

Topics: Bioinformatics and Systems Biology; Image Understanding; Object Recognition; Medical Imaging

Authors: Yuji Iwahori ¹ , Hiroaki Hagi ¹ , Hiroyasu Usami ¹ , Robert J. Woodham ² , Aili Wang ³ , M. K. Bhuyan ⁴ and Kunio Kasugai ⁵

Affiliations: ¹ Chubu University, Japan , ² University of British Columbia, Canada , ³ Harbin University of Science and Technology, China , ⁴ Indian Institute of Technology Guwahati, India , ⁵ Aichi Medical University, Japan

Digital Object Identifier: 10.5220/0006189704020409

Proceedings of ICPRAM 2017, ISBN: 978-989-758-222-6

Quick Abstract

An endoscope is a medical instrument that acquires images inside the human body. This paper proposes a new approach for the automatic detection of polyp regions in an endoscope image by generating a likelihood map with both of edge and color information to obtain high accuracy so that probability becomes high at around polyp candidate region. Next, Histograms of Oriented Gradients (HOG) features are extracted from the detected region and random forests are applied for the classification to judge whether the detected region is polyp region or not. It is shown that the proposed approach has high accuracy for the polyp detection and the usefulness is confirmed through the computer experiments with endoscope images.

Related Ontology Subjects/Areas/Topics (8)

Applications	Image Understanding	Pattern Recognition
Bioinformatics and Systems Biology	Medical Imaging	Software Engineering
Computer Vision, Visualization and Computer Graphics	Object Recognition

Automatic Abstraction of Flow of Control in a System of Distributed Software Components

Topics: Monitoring of Services, Quality of Service, Service Level Agreements; Cloud Middleware Frameworks; Cloud Applications Performance and Monitoring; API Management

Authors: Nima Kaviani ¹ , Michael Maximilien ² , Ignacio Silva-Lepe ² and Isabelle Rouvellou ²

Affiliations: ¹ University of British Columbia, Canada , ² IBM Watson Research Center, United States

Digital Object Identifier: 10.5220/0005407403810388

Proceedings of CLOSER 2015, ISBN: 978-989-758-104-5

Quick Abstract

CloudFoundry (CF) provides an open source platform-as-a-service software for deploying scalable software systems to the cloud. The architecture for CF is distributed by design and consists of several components which interact with one another through a message-oriented middleware. This message-oriented distributed design delivers on the scalability and resiliency requirements of the platform. In such a complex distributed multicomponent system, there is a steep learning curve for software developers to understand how components interact, what messages are exchanged between them, and how the message exchanges affect the behaviour of the system. In particular developers find it difficult to identify the execution flows, the authentication flows, interactions with the persistence layer, etc. We have developed a framework that allows interpreting the behaviour of the system by analyzing the exchanged messages between components, inspecting message contents, and extracting data and control flow across components. The paramount aim is to improve developers’ understandability of the system and to examine software resiliency through approaches like bug injection and message alterations. An initial version of our framework was released to the CF community and we have collected feedback that indeed show that we are achieving some of our goals.

Related Ontology Subjects/Areas/Topics (7)

API Management	Cloud Computing Enabling Technology	Monitoring of Services, Quality of Service, Service Level Agreements
Cloud Applications Performance and Monitoring	Cloud Middleware Frameworks	Platforms and Applications
Cloud Computing

Co-Design of Information Systems with Digital Records Management - A Proposal for Research

Topics: Enterprise Information Systems; Business Process Management

Author: Sherry L. Xie

Affiliation: Center for Electronic Records Management Research, Ministry of Education and Renmin Univeristy of China, China

Digital Object Identifier: 10.5220/0005617202220228

Proceedings of KMIS 2015/IC3K, ISBN: 978-989-758-158-8

Quick Abstract

This position paper, via rationalizing the potentials of carrying out a carefully crafted research project, argues that the three fields of enterprise information system, business process management, and digital records management should join forces in serving the needs of organizations, regardless of their types. By preliminary findings uncovered during the conceiving process of the project, it advocates that the best point of convergence is the design phase, a basic step for all of the three fields and an increasingly critical one in today’s digital environment.

Related Ontology Subjects/Areas/Topics (8)

Artificial Intelligence	Enterprise Engineering	Knowledge-Based Systems
Business Process Management	Enterprise Information Systems	Symbolic Systems
e-Business	Knowledge Management and Information Sharing

DeLi2P - A User Centric, Scalable Demand Side Management Strategy for Smart Grids

Topics: Load Balancing in Smart Grids; Optimization Techniques for Efficient Energy Consumption; Energy Management Systems (EMS)

Authors: Syed Muhammad Ali ¹ , Mohammad Naveed ² , Fahad Javed ³ , Naveed Arshad ² and Jahangir Ikram ²

Affiliations: ¹ University of British Columbia, Canada , ² Lahore University of Management Sciences, Pakistan , ³ GIFT University, Pakistan

Digital Object Identifier: 10.5220/0005437301480156

Proceedings of SMARTGREENS 2015, ISBN: 978-989-758-105-2

Quick Abstract

Smart grids coulpled with effiicient demand side management (DSM) is an important step for greener cities of the future. DSM has the potential to significantly improve smart grid operations by reducing the peak to average ratio. Current DSM schemes are able to reduce peak load by as much as 30% which can translate to significant cost savings and reduction in green house emissions. But for realistic deployment of DSM systems in the grid there are two very important aspects which need to be considered: scalability and user acceptability. Since the current DSM algorithms are required to control potentially hundreds of thousands of devices, they have to be scalable and tractable for such myriad numbers. On the other hand DSM affects the life style of the consumer and this should be as less disruptive as possible. The various DSM techniques proposed in the literature attempt to first reduce the cost and then attempt to resolve one of the two aforementioned aspects. The result is that the techniques are either scalable or are only considerate of the deadlines of the consumers. An ideal system should cater to both of these aspects. Our system Deli2P is user centric and scalable thus catering to both of these aspects. Essentially we provide to the consumer a deadline centric interface. The deadlines solutions are generally not scalable. But instead of solving this problem as a scheduling for deadline problem we transform the problem to a priority-based problem thus making it scalable for large number of devices. Our results show that with this scheme we can reduce peak power by upto 30% but without violating consumers` deadlines.

Related Ontology Subjects/Areas/Topics (6)

Energy and Economy		Energy-Aware Systems and Technologies		Optimization Techniques for Efficient Energy Consumption
Energy Management Systems (EMS)		Load Balancing in Smart Grids		Smart Grids

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