TopKube: A Rank-Aware Data Cube for Real-Time Exploration of Spatiotemporal Data

2018 ◽  
Vol 24 (3) ◽  
pp. 1394-1407 ◽  
Author(s):  
Fabio Miranda ◽  
Lauro Lins ◽  
James T. Klosowski ◽  
Claudio T. Silva
Keyword(s):  
Real Time ◽  
Data Cube ◽  
Spatiotemporal Data

A multi-agent system for distributed smartphone sensing cycling in smart cities

2020 ◽  
Vol 22 (1) ◽  
pp. 119-134
Author(s):  
Theodoros Anagnostopoulos ◽  
Chu Luo ◽  
Jino Ramson ◽  
Klimis Ntalianis ◽  
Vassilis Kostakos ◽  
...  
Keyword(s):  
Real Time ◽  
Smart City ◽  
Spatiotemporal Data ◽  
Inference Model ◽  
Data Set ◽  
Smartphone Sensing ◽  
Content Type ◽  
Traffic Lights ◽  
Model Complex ◽  
Multi Agent

Purpose The purpose of this paper is to propose a distributed smartphone sensing-enabled system, which assumes an intelligent transport signaling (ITS) infrastructure that operates traffic lights in a smart city (SC). The system is able to handle priorities between groups of cyclists (crowd-cycling) and traffic when approaching traffic lights at road junctions. Design/methodology/approach The system takes into consideration normal probability density function (PDF) and analytics computed for a certain group of cyclists (i.e. crowd-cycling). An inference model is built based on real-time spatiotemporal data of the cyclists. As the system is highly distributed – both physically (i.e. location of the cyclists) and logically (i.e. different threads), the problem is treated under the umbrella of multi-agent systems (MAS) modeling. The proposed model is experimentally evaluated by incorporating a real GPS trace data set from the SC of Melbourne, Australia. The MAS model is applied to the data set according to the quantitative and qualitative criteria adopted. Cyclists’ satisfaction (CS) is defined as a function, which measures the satisfaction of the cyclists. This is the case where the cyclists wait the least amount of time at traffic lights and move as fast as they can toward their destination. ITS system satisfaction (SS) is defined as a function that measures the satisfaction of the ITS system. This is the case where the system serves the maximum number of cyclists with the fewest transitions between the lights. Smart city satisfaction (SCS) is defined as a function that measures the overall satisfaction of the cyclists and the ITS system in the SC based on CS and SS. SCS defines three SC policies (SCP), namely, CS is maximum and SS is minimum then the SC is cyclist-friendly (SCP1), CS is average and SS is average then the SC is equally cyclist and ITS system friendly (SCP2) and CS is minimum and SS is maximum then the SC is ITS system friendly (SCP3). Findings Results are promising toward the integration of the proposed system with contemporary SCs, as the stakeholders are able to choose between the proposed SCPs according to the SC infrastructure. More specifically, cyclist-friendly SCs can adopt SCP1, SCs that treat cyclists and ITS equally can adopt SCP2 and ITS friendly SCs can adopt SCP3. Originality/value The proposed approach uses internet connectivity available in modern smartphones, which provide users control over the data they provide to us, to obviate the installation of additional sensing infrastructure. It extends related study by assuming an ITS system, which turns traffic lights green by considering the normal PDF and the analytics computed for a certain group of cyclists. The inference model is built based on the real-time spatiotemporal data of the cyclists. As the system is highly distributed – both physically (i.e. location of the cyclists) and logically (i.e. different threads), the system is treated under the umbrella of MAS. MAS has been used in the literature to model complex systems by incorporating intelligent agents. In this study, the authors treat agents as proxy threads running in the cloud, as they require high computation power not available to smartphones.

The Po-tree: a Real-time Spatiotemporal Data Indexing Structure

2006 ◽  
pp. 259-270 ◽  
Author(s):  
Guillaume Noël ◽  
Sylvie Servigne ◽  
Robert Laurini
Keyword(s):  
Real Time ◽  
Spatiotemporal Data ◽  
Data Indexing ◽  
Indexing Structure

scholarly journals INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS

2015 ◽  
Vol II-4/W2 ◽  
pp. 67-71 ◽  
Author(s):  
A. Bhushan ◽  
M. H. Sharker ◽  
H. A. Karimi
Keyword(s):  
Principal Component Analysis ◽  
Real Time ◽  
Outlier Detection ◽  
Data Streams ◽  
Credit Card ◽  
Principal Component ◽  
Component Analysis ◽  
Spatiotemporal Data ◽  
Sensor Data ◽  
Detection Methods

In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

scholarly journals Applications of Big Data Analytics within a Dynamic Simulation Modeling Platform to Inform Osteoarthritis Care in Alberta

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Behnam Sharif ◽  
Shelly Vik ◽  
Deborah A Marshall-Catlin
Keyword(s):  
Simulation Model ◽  
Real Time ◽  
Administrative Data ◽  
Average Cost ◽  
Input Parameter ◽  
Service Planning ◽  
Data Cube ◽  
Multidimensional Data ◽  
Number Of Patients ◽  
Input Parameters

IntroductionOsteoarthritis (OA) is a leading cause of chronic disability. There is need to leverage administrative data to support OA policy analysis. Our objective was to develop and apply a multidimensional data cube as an input parameter repository using health administrative data to populate an OA simulation model. Objectives and ApproachHealth administrative data including practitioner claims, inpatient and ambulatory visits from 1994 to 2013 were integrated into a multidimensional data cube. OA cases were identified using validated algorithms, and followed through stages of care (primary, specialist, acute and post-operative). The cube provided rate calculations, duration and average cost for each stage of care across the model dimensions (age categories, sex, comorbidity status and geographic zones). The rates were then linked to the model as input parameters to simulate patient flow across the continuum of care. We used the model to predict direct costs across all dimensions from 2010 to 2035. ResultsUsing the model, total number of patients with OA in Alberta will increase from 312,000 in 2010 to 1.4 million in 2035. The average annual cost per OA patient also increases from $2,800 to $4,900, and the total cost increases from $450 million in 2010 to 2.2 billion in 2035. The majority of the patients were at earlier stages (non-surgical 78%, surgical 22%), with lower average cost (non-surgical $3,300 vs. surgical $16,400) in 2010. As new administrative data are being provided routinely, the data cube is capable of providing real-time updates for the input parameters of the model, which will aid in validation of the model results and improving the precision of projections. Conclusion/ImplicationsThe data cube has significantly improved our ability to manage and analyze administrative data within a simulation model to project the burden of OA in Alberta. The integrated model can be used as a real time decision-support tool to inform osteoarthritis service planning and variations in resource utilization.

Parallel Real-Time OLAP on Multi-Core Processors

2015 ◽  
Vol 11 (1) ◽  
pp. 23-44 ◽  
Author(s):  
Frank Dehne ◽  
Hamidreza Zaboli
Keyword(s):  
Decision Support ◽  
Response Time ◽  
Real Time ◽  
Large Scale ◽  
Computing Methods ◽  
Data Cube ◽  
Data Set ◽  
Performance Improvements ◽  
Decision Support Process ◽  
Processor Cores

One of the most powerful and prominent technologies for knowledge discovery in decision support systems is online analytical processing (OLAP). Most of the traditional OLAP research, and most of the commercial systems, follow the static data cube approach proposed by Gray et.al. and materialize all or a subset of the cuboids of the data cube in order to ensure adequate query performance. Practitioners have called for some time for a real-time OLAP approach where the OLAP system gets updated instantaneously as new data arrives and always provides an up-to-date data warehouse for the decision support process. However, a major problem for real-time OLAP is the significant performance issues with large scale data warehouses. The aim of our research is to address these problems through the use of efficient parallel computing methods. In this paper, we present a parallel real-time OLAP system for multi-core processors. To our knowledge, this is the first real-time OLAP system that has been parallelized and optimized for contemporary multi-core architectures. Our system allows for multiple insert and multiple query transactions to be executed in parallel and in real-time. We evaluated our method for a multitude of scenarios (different ratios of insert and query transactions, query transactions with different amounts of data aggregation, different database sizes, etc.), using the TPCDS “Decision Support” benchmark data set. As multi-core test platforms, we used an Intel Sandy Bridge processor with 4 cores (8 hardware supported threads) and an Intel Xeon Westmere processor with 20 cores (40 hardware supported threads). The tests demonstrate that, with increasing number of processor cores, our parallel system achieves close to linear speedup in transaction response time and transaction throughput. On the 20 core architecture we achieved, for a 100 GB database, a better than 0.25 second query response time for real-time OLAP queries that aggregate 25% of the database. Since hardware performance improvements are currently, and in the foreseeable future, achieved not by faster processors but by increasing the number of processor cores, our new parallel real-time OLAP method has the potential to enable OLAP systems that operate in real-time on large databases.

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