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## Heterogeneous stream processing and crowdsourcing for urban traffic management (2014)

Venue: | IN PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON EXTENDING DATABASE TECHNOLOGY |

Citations: | 5 - 3 self |

### Citations

11690 | Maximum likelihood from incomplete data via the EM algorithm
- Dempster, Laird, et al.
- 1977
(Show Context)
Citation Context ...e traffic modelling component. In what follows, we describe our crowdsourcing model and briefly review the process of reliability estimation with the classical Expectation-Maximization (EM) algorithm =-=[8, 22]-=-. This algorithm needs to operate in batch mode, which is not acceptable for our large, streaming problem. Consequently, we then discuss an online version of the EM algorithm that supports online crow... |

3207 | MapReduce: Simplified data processing on large clusters
- Dean, Ghemawat
- 2004
(Show Context)
Citation Context ...rom the user to reach him, and (ii) adaptive mechanisms that achieve real-time and reliable communication. To maximize parallelism, the crowdsourcing component employs the MapReduce programming model =-=[7, 14]-=- to communicate the queries to the selected participants and enable them to do local processing. MapReduce is a computational paradigm that allows processing parallelizable tasks across distributed no... |

751 |
A logic-based calculus of events
- Kowalski, Sergot
- 1986
(Show Context)
Citation Context ... second processor also estimates participant reliability. 4. COMPLEX EVENT PROCESSING Our CE recognition component is based on the Event Calculus for Run-Time reasoning (RTEC) [2]. The Event Calculus =-=[15]-=- is a logic programming language for representing and reasoning about events and their effects. The benefits of a logic programming approach to CE recognition are welldocumented: such an approach has ... |

337 |
The Power of Events: An Introduction to Complex Event Processing
- Luckham
(Show Context)
Citation Context ...zens to constantly interact using mobile sensors. Detecting complex events from heterogeneous data streams is a promising vehicle to support applications for monitoring, detection and online response =-=[11, 20]-=-. Consider e.g. an urban monitoring system that identifies traffic congestions (in-themake) and (proactively) changes traffic light priorities and speed limits to reduce ripple effects. Such a system ... |

241 | Get another label? Improving data quality and data mining using multiple, noisy labelers
- Sheng, Provost, et al.
- 2008
(Show Context)
Citation Context ...del how reliable each participant is, and use participant reliability to improve the aggregation of answers. To this end, the Expectation-Maximization (EM) algorithm [23], Bayesian uncertainty scores =-=[24]-=- and sequential Bayesian estimation [10] have been used. We present a crowdsourcing component that queries participants close to the location of a source disagreement event whenever requested by the C... |

232 | Kernels and regularization on graphs
- Smola, Kondor
- 2003
(Show Context)
Citation Context ...ether in an graph G, the covariances are closely related to the network structure: the variables are highly correlated if they are adjacent in G, and vice versa. Therefore we can employ graph kernels =-=[27]-=- to denote the covariance functions k̂(xi, xj) among the locations xi and xj , and thus the covariance matrix K̂. The work in [18, 17] describes methods to incorporate knowledge on preferred routes in... |

178 | Learning from crowds
- Raykar, Yu, et al.
(Show Context)
Citation Context ...ch area. Many approaches try to model how reliable each participant is, and use participant reliability to improve the aggregation of answers. To this end, the Expectation-Maximization (EM) algorithm =-=[23]-=-, Bayesian uncertainty scores [24] and sequential Bayesian estimation [10] have been used. We present a crowdsourcing component that queries participants close to the location of a source disagreement... |

126 |
Markov Logic: An Interface Layer for Artificial Intelligence
- Domingos, Lowd
- 2009
(Show Context)
Citation Context ...his issue. Probabilistic event recognition techniques may be employed in order to deal with this type of uncertainty. Consider, for example, probabilistic graphical models [28], Markov Logic Networks =-=[9, 26]-=-, probabilistic logic programming [25], and fuzzy set and possibility theory [19]. Although there is considerable work on optimising probabilistic reasoning techniques, the imposed overhead in the pre... |

42 |
On-line expectation–maximization algorithm for latent data models
- CAPPÉ, MOULINES
- 2009
(Show Context)
Citation Context ...ll) subset of participants for each event. Hence, if we operate on a subset of events, there’s a risk we may discard all the answers of a participant. Therefore, we use instead an online EM algorithm =-=[6]-=-. This algorithm can operate on one source disagreement event at the time, and both the event and the associated answers can be forgotten once this event has been processed. Discarding this informatio... |

32 |
Vox populi
- Galton
- 1907
(Show Context)
Citation Context ...for each data item and combining them to produce a more accurate label. E.g. it has been known that the error of the average answer is usually smaller than the average error of each individual answer =-=[12]-=-. Developing increasingly better strategies to aggregate individual answers is an open research area. Many approaches try to model how reliable each participant is, and use participant reliability to ... |

29 | A probabilistic framework to learn from multiple annotators with time-varying accuracy
- Donmez, Carbonell, et al.
- 2010
(Show Context)
Citation Context ...d use participant reliability to improve the aggregation of answers. To this end, the Expectation-Maximization (EM) algorithm [23], Bayesian uncertainty scores [24] and sequential Bayesian estimation =-=[10]-=- have been used. We present a crowdsourcing component that queries participants close to the location of a source disagreement event whenever requested by the CE processing component. The 5www.mturk.c... |

29 | Modeling uncertainties in publish/subscribe systems
- Liu, Jacobsen
- 2004
(Show Context)
Citation Context ...al with this type of uncertainty. Consider, for example, probabilistic graphical models [28], Markov Logic Networks [9, 26], probabilistic logic programming [25], and fuzzy set and possibility theory =-=[19]-=-. Although there is considerable work on optimising probabilistic reasoning techniques, the imposed overhead in the presence of large data streams, such as those of Dublin, does not allow for real-tim... |

14 |
The EM algorithm and extensions, volume 382
- McLachlan, Krishnan
- 2007
(Show Context)
Citation Context ...e traffic modelling component. In what follows, we describe our crowdsourcing model and briefly review the process of reliability estimation with the classical Expectation-Maximization (EM) algorithm =-=[8, 22]-=-. This algorithm needs to operate in batch mode, which is not acceptable for our large, streaming problem. Consequently, we then discuss an online version of the EM algorithm that supports online crow... |

13 | Run-time composite event recognition
- Artikis, Sergot, et al.
(Show Context)
Citation Context ...king congestion estimates at locations with low sensor coverage is wrapped as a Streams service. For complex event processing, our solution relies on the Event Calculus for Run-Time reasoning (RTEC)3 =-=[2]-=-, a Prolog-based engine, which is detailed below. We integrated RTEC by a dedicated processor in Streams that would forward the received SDEs to an RTEC instance using a bidirectional Java-Prolog-Inte... |

12 |
Efficient processing of uncertain events in rule-based systems
- Wasserkrug, Gal, et al.
- 2012
(Show Context)
Citation Context ... several ways to deal with this issue. Probabilistic event recognition techniques may be employed in order to deal with this type of uncertainty. Consider, for example, probabilistic graphical models =-=[28]-=-, Markov Logic Networks [9, 26], probabilistic logic programming [25], and fuzzy set and possibility theory [19]. Although there is considerable work on optimising probabilistic reasoning techniques, ... |

10 |
Event processing glossary — version 1.1
- Luckham, Schulte
- 2008
(Show Context)
Citation Context ...his aspect is highlighted by the notion of a simple, derived event (SDE), which is the result of applying a computational derivation process to some other event, such as an event coming from a sensor =-=[21]-=-. A stream of such time-stamped SDEs is the primary input of our system. Additionally, the system may solicit input from citizens using a connected crowdsourcing component. The output of crowdsourcing... |

10 |
A probabilistic logic programming event calculus,” Theory and
- Skarlatidis, Artikis, et al.
- 2013
(Show Context)
Citation Context ...n techniques may be employed in order to deal with this type of uncertainty. Consider, for example, probabilistic graphical models [28], Markov Logic Networks [9, 26], probabilistic logic programming =-=[25]-=-, and fuzzy set and possibility theory [19]. Although there is considerable work on optimising probabilistic reasoning techniques, the imposed overhead in the presence of large data streams, such as t... |

9 |
The streams Framework
- Bockermann, Blom
(Show Context)
Citation Context ...he backbone of our solution is a stream processing component, which couples the output from the sensors with further data analysis components. Stream processing is realized with the Streams framework =-=[4]-=-. It provides a language for the description of data flow graphs, which are then compiled into a computation graph for a stream processing engine. Data Analysis Components: The system uses components ... |

9 |
Galaxy Zoo: the large-scale spin statistics of spiral galaxies in the Sloan Digital Sky Survey.Monthly Notices of the Royal Astronomical Society 388
- Land, Slosar, et al.
- 2008
(Show Context)
Citation Context ...sourcing has enjoyed a recent rise in popularity due to the development of dedicated online tools, such as Amazon Mechanical Turk5, and has been used for many complex tasks such as labelling galaxies =-=[16]-=-, real-time worker selection [5] and solving various biological problems [13]. The main appeal of crowdsourcing is the reduced cost of label acquisition. Typically, the lower quality of the labels is ... |

7 | Probabilistic event calculus based on markov logic networks
- Skarlatidis, Paliouras, et al.
- 2011
(Show Context)
Citation Context ...his issue. Probabilistic event recognition techniques may be employed in order to deal with this type of uncertainty. Consider, for example, probabilistic graphical models [28], Markov Logic Networks =-=[9, 26]-=-, probabilistic logic programming [25], and fuzzy set and possibility theory [19]. Although there is considerable work on optimising probabilistic reasoning techniques, the imposed overhead in the pre... |

6 | Event processing under uncertainty
- Artikis, Etzion, et al.
- 2012
(Show Context)
Citation Context ...considerable work on optimising probabilistic reasoning techniques, the imposed overhead in the presence of large data streams, such as those of Dublin, does not allow for real-time event recognition =-=[1]-=-. In [3], we used variety of input data to handle veracity. The events detected on the bus data stream were matched against the events detected on the SCATS stream to identify mismatches that indicate... |

6 |
Pedestrian quantity estimation with trajectory patterns
- Liebig, Xu, et al.
- 2012
(Show Context)
Citation Context .... The traffic network contains prior knowledge on movement through the city of Dublin. We model the edge oriented quantities within a Gaussian Process regression framework, similar to the approach in =-=[18]-=-. In the traffic graph G each junction corresponds to one vertex. To each vertex vi in the graph, we introduce a latent variable fi which represents the true traffic flow at vi. The observed traffic f... |

4 |
Incorporating mobility patterns in pedestrian quantity estimation and sensor placement
- Liebig, Xu, et al.
- 2013
(Show Context)
Citation Context ... adjacent in G, and vice versa. Therefore we can employ graph kernels [27] to denote the covariance functions k̂(xi, xj) among the locations xi and xj , and thus the covariance matrix K̂. The work in =-=[18, 17]-=- describes methods to incorporate knowledge on preferred routes in the kernel matrix. Lacking this information, we opt for the commonly used regularized Laplacian kernel function K̂ = [ β(L+ I/α2) ]−1... |

3 | Self-adaptive event recognition for intelligent transport management
- Artikis, Weidlich, et al.
- 2013
(Show Context)
Citation Context ...dition, data present a sparsity problem, since the traffic in several locations in the city is either never monitored due to lack of sensors, or infrequently monitored (e.g. when a bus passes by). In =-=[3]-=-, we outlined the principle of using variety of input data to effectively handle veracity. Streams from multiple sources were leveraged to generate common complex events. A complex event processing co... |

3 |
Crowdsourcing for bioinformatics. Bioinformatics 2013:btt333
- Good, Su
(Show Context)
Citation Context ...dicated online tools, such as Amazon Mechanical Turk5, and has been used for many complex tasks such as labelling galaxies [16], real-time worker selection [5] and solving various biological problems =-=[13]-=-. The main appeal of crowdsourcing is the reduced cost of label acquisition. Typically, the lower quality of the labels is compensated by acquiring several labels for each data item and combining them... |

3 |
Misco: A system for data analysis applications on networks of smartphones using mapreduce
- Kakantousis, Boutsis, et al.
- 2012
(Show Context)
Citation Context ...rom the user to reach him, and (ii) adaptive mechanisms that achieve real-time and reliable communication. To maximize parallelism, the crowdsourcing component employs the MapReduce programming model =-=[7, 14]-=- to communicate the queries to the selected participants and enable them to do local processing. MapReduce is a computational paradigm that allows processing parallelizable tasks across distributed no... |

2 |
Crowdsourcing under Real-Time constraints
- Boutsis, Kalogeraki
- 2013
(Show Context)
Citation Context ...e in popularity due to the development of dedicated online tools, such as Amazon Mechanical Turk5, and has been used for many complex tasks such as labelling galaxies [16], real-time worker selection =-=[5]-=- and solving various biological problems [13]. The main appeal of crowdsourcing is the reduced cost of label acquisition. Typically, the lower quality of the labels is compensated by acquiring several... |