Any important data management and analytics tasks cannot be completely addressed by automated processes. These tasks, such as entity resolution, sentiment analysis, and image recognition can be enhanced through the use of human cognitive ability. Crowdsouring platforms are an effective way to harness the capabilities of people (i.e., the crowd) to apply human computation for such tasks. Thus, crowdsourced data management has become an area of increasing interest in research and industry. We identify three important problems in crowdsourced data management. (1) Quality Control: Workers may return noisy or incorrect results so effective techniques are required to achieve high quality; (2) Cost Control: The crowd is not free, and cost control aims to reduce the monetary cost; (3) Latency Control: The human workers can be slow, particularly compared to automated computing time scales, so latency-control techniques are required. There has been significant work addressing these three factors for designing crowdsourced tasks, developing crowdsourced data manipulation operators, and optimizing plans consisting of multiple operators. In this paper, we survey and synthesize a wide spectrum of existing studies on crowdsourced data management. Based on this analysis we then outline key factors that need to be considered to improve crowdsourced data management.

/pdf/crowdsourced-data-management-a-survey-1a5fjazc94.pdf

Crowdsourced Data Management: A Survey

Detection of Intrusions and Malware, and Vulnerability Assessment

An Introduction To Modern Astrophysics

Multistep traffic forecasting on road networks is a crucial task in successful intelligent transportation system applications. To capture the complex non-stationary temporal dynamics and spatial correlations in multistep traffic-condition prediction, we propose a novel deep learning framework named attention graph convolutional sequence-to-sequence model (AGC-Seq2Seq). In the proposed deep learning framework, spatial and temporal dependencies are modeled through the Seq2Seq model and graph convolution network separately, and the attention mechanism along with a newly designed training method based on the Seq2Seq architecture is proposed to overcome the difficulty in multistep prediction and further capture the temporal heterogeneity of traffic pattern. We conduct numerical tests to compare AGC-Seq2Seq with other benchmark models using two real-world datasets. The results indicate that our model yields the best prediction performance in terms of various prediction error measures. Furthermore, the variations of spatio-temporal correlations of traffic conditions under different perdition steps and road segments are revealed.

Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies

Crowdsourcing is a computing paradigm where humans are actively involved in a computing task, especially for tasks that are intrinsically easier for humans than for computers. Spatial crowdsourcing is an increasing popular category of crowdsourcing in the era of mobile Internet and sharing economy, where tasks are spatiotemporal and must be completed at a specific location and time. In fact, spatial crowdsourcing has stimulated a series of recent industrial successes including sharing economy for urban services (Uber and Gigwalk) and spatiotemporal data collection (OpenStreetMap and Waze). This survey dives deep into the challenges and techniques brought by the unique characteristics of spatial crowdsourcing. Particularly, we identify four core algorithmic issues in spatial crowdsourcing: (1) task assignment, (2) quality control, (3) incentive mechanism design, and (4) privacy protection. We conduct a comprehensive and systematic review of existing research on the aforementioned four issues. We also analyze representative spatial crowdsourcing applications and explain how they are enabled by these four technical issues. Finally, we discuss open questions that need to be addressed for future spatial crowdsourcing research and applications.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5938&context=sis_research

Spatial crowdsourcing: a survey

Dynamic route planning with real-time traffic predictions

Urban trac gathers increasing interest as cities become bigger, crowded and \smart". We present a system for heterogeneous stream processing and crowdsourcing supporting intelligent urban trac management. Complex events related to trac congestion (trends) are detected from heterogeneous sources involving xed sensors mounted on intersections and mobile sensors mounted on public transport vehicles. To deal with data veracity, a crowdsourcing component handles and resolves sensor disagreement. Furthermore, to deal with data sparsity, a trac modelling component oers information in areas with low sensor coverage. We demonstrate the system with a real-world use-case from Dublin city, Ireland.

/pdf/heterogeneous-stream-processing-and-crowdsourcing-for-urban-1ona7g9zkl.pdf

Heterogeneous Stream Processing and Crowdsourcing for Urban Traffic Management

Malicious software (malware) represents a major threat for computer systems of almost all types. In the past few years the number of prevalent malware samples has increased dramatically due to the fact that malware authors started to deploy morphing (aka obfuscation) techniques in order to hinder detection of such polymorphic malware by anti-malware products. Using these techniques numerous variants of a malware can be generated. All these variants have a different syntactic representation while providing almost the same functionality and showing similar behavior. In order to effectively detect polymorphic malware it is advantageous (if not required) to know which malware samples are variants of a particular malware. Respective approaches for determining this relation between malware samples automatically are currently investigated by a number of researchers. A prerequisite for assessing this relation based on particular features of malware samples is an appropriate similarity or distance measure. In particular a number of approaches for clustering malware samples have been recently published. Thereby different similarity measures are used but without thoroughly discussing their choice. So it is an unanswered question which similarity measures are appropriate for determining respective relations between malware samples. To answer this question we study different distance measures in detail and discuss desirable properties of a distance measure for this particular purpose. We focus on behavioral features of malware and compare and experimentally evaluate different distance measures for malware behavior. Based on our results we identify a most appropriate distance measure for grouping malware samples based on similar behavior.

Measuring similarity of malware behavior

Smart route planning gathers increasing interest as cities become crowded and jammed. We present a system for individual trip planning that incorporates future traffic hazards in routing. Future traffic conditions are computed by a Spatio-Temporal Random Field based on a stream of sensor readings. In addition, our approach estimates traffic flow in areas with low sensor coverage using a Gaussian Process Regression. The conditioning of spatial regression on intermediate predictions of a discrete probabilistic graphical model allows to incorporate historical data, streamed online data and a rich dependency structure at the same time. We demonstrate the system and test model assumptions with a real-world use-case from Dublin city, Ireland.

/pdf/predictive-trip-planning-smart-routing-in-smart-cities-3f2efqoqrg.pdf

Predictive Trip Planning - Smart Routing in Smart Cities

Modern multi-tier application systems are generally based on high performance database systems in order to process and store business information Containing valuable business information, these systems are highly interesting to attackers and special care needs to be taken to prevent any malicious access to this database layer In this work we propose a novel approach for modelling SQL statements to apply machine learning techniques, such as clustering or outlier detection, in order to detect malicious behaviour at the database transaction level The approach incorporates the parse tree structure of SQL queries as characteristic eg for correlating SQL queries with applications and distinguishing benign and malicious queries We demonstrate the usefulness of our approach on real-world data

/pdf/learning-sql-for-database-intrusion-detection-using-context-21o12zkmk4.pdf

Christian Bockermann

Papers

Dynamic route planning with real-time traffic predictions

Heterogeneous Stream Processing and Crowdsourcing for Urban Traffic Management

Measuring similarity of malware behavior

Predictive Trip Planning - Smart Routing in Smart Cities

Learning SQL for Database Intrusion Detection Using Context-Sensitive Modelling (Extended Abstract)