Cybersickness remains a major barrier to the widespread adoption of virtual reality (VR) technologies, motivating researchers to investigate its causes and mitigation strategies through comparative human-subjects studies. These experiments may employ either within- or between-subjects designs. Although within-subjects designs require fewer participants and potentially offer higher statistical power, researchers need to wash out carryover effects of cybersickness symptoms before each session to avoid confounding the results. Prior studies have employed washout periods of varying lengths; some required testing conditions on different days, while others allowed only short breaks of less than 15 minutes. Although shorter washout periods are more convenient for experimenters, their impact on study outcomes has not been systematically investigated. In this work, we conducted a meta-analysis to evaluate the effects of study design and washout period length on cybersickness self-reports measured with the Simulator Sickness Questionnaire (SSQ). We found that short washout periods reduce statistical power compared to long washout periods or between-subjects designs. Based on these findings, we provide guidelines to help researchers design more reliable cybersickness studies and improve the generalizability of their results.

Spatiotemporal sensitivity to high frequency information declines with increased peripheral eccentricity. Foveated rendering exploits this by decreasing the spatial resolution of rendered images in peripheral vision, reducing the rendering cost by omitting high frequency details. As foveation levels increase, the rendering quality is reduced, and traditional foveated rendering systems tend not to preserve samples that were previously rendered at high spatial resolution in previous frames. Additionally, prior research has shown that saccade landing positions are distributed around a target location rather than landing at a single point, and that even during fixations, eyes perform small microsaccades around a fixation point. This creates an opportunity for sampling from temporally neighbouring frames with differing foveal locations to reduce the required rendered size of the foveal region while achieving a higher perceived image quality. We further observe that the temporal presentation of pixels frame-to-frame can be viewed as a data stream, presenting a random sampling problem. Following this intuition, we propose a Weighted Reservoir Sampling technique to efficiently maintain a reservoir of the perceptually relevant high quality pixel samples from previous frames and incorporate them into the computation of the current frame. This allows the renderer to render a smaller region of foveal pixels per frame by temporally reusing pixel samples that are still relevant to reconstruct a higher perceived image quality, while allowing for higher levels of foveation. Our method operates on the output of foveated rendering, and runs in under 1 ms at 4K resolution, making it highly efficient and integrable with real-time VR and AR foveated rendering systems.

This paper investigates the influence of X-ray vision metaphors on distance estimation in optical-see-through augmented reality (AR) in action space. A within-subjects study $(\mathrm{N}=30)$ was conducted to evaluate depth judgments across five conditions, including a novel “invisible mesh” technique. Participants performed a series of blind walking tasks that required estimating the depth of AR objects displayed at multiple distance ranges in front or behind a physical occluding surface. Although quantitative results regarding the impact of different X-ray vision metaphors on distance perception were inconclusive, participant feedback revealed a diversity of strategies and preferences. Overall, the findings suggest that no single metaphor was considered universally superior, and multiple X-ray vision metaphors may be suitable for different users and situations. This research contributes to understanding of X-ray vision techniques and informs the design considerations for AR systems aiming to enhance depth perception and user experience.

In modern big data warehouse systems, we observe a common phenomenon that a column of data values can be derived from one or several other columns by transforming and concatenating these columns. We call this relationship between columns a Soft Concatenation Mapping (SCM). SCMs imply significant redundancy in the schema or data, and therefore can be exploited for data integration or data compression. In this paper, we formalize the problem of SCM detection and prove it is NP-hard. We then propose efficient approximate algorithms to detect all SCMs or an optimal set of SCMs in a table. Our experiments on both real-world and synthetic datasets show promising results.

The value of large amount of location‐based mobile data has received wide attention in many research fields including human behavior analysis, urban transportation planning, and various location‐based services. Nowadays, both scientific and industrial communities are encouraged to collect as much location‐based mobile data as possible, which brings two challenges: (1) how to efficiently process the queries of big location‐based mobile data and (2) how to reduce the cost of storage services, because it is too expensive to store several exact data replicas for fault‐tolerance. So far, several dedicated storage systems have been proposed to address these issues. However, they do not work well when the ranges of queries vary widely. In this work, we design a storage system based on diverse replica scheme which not only can improve the query processing efficiency but also can reduce the cost of storage space. To the best of our knowledge, this is the first work to investigate the data storage and processing in the context of big location‐based mobile data. Specifically, we conduct in‐depth theoretical and empirical analysis of the trade‐offs between different spatial‐temporal partitioning and data encoding schemes. Moreover, we propose an effective approach to select an appropriate set of diverse replicas, which is optimized for the expected query loads while conforming to the given storage space budget. The experiment results show that using diverse replicas can significantly improve the overall query performance and the proposed algorithms for the replica selection problem are both effective and efficient.

In this paper, we present TICC, an automatic data compression component that can transparently eliminate data redundancies across columns in column-oriented database systems. We further propose two approaches to integrate inter-column compression into existing database systems. One approach is to use User Defined Functions (UDFs), and the other is native. We implement these two approaches on top of Hive based on the ORC file, a common data format in column stores, and evaluate the performance of TICC using real-world datasets. The experimental results demonstrate that TICC can significantly reduce the storage overhead and process a variety of queries over large-scale data with up to 20% performance improvement over the original Hive.

The maintenance of batteries used in wireless mobile communication is an important practical problem. The experts can easily recognize the battery events, such as turning on, by watching the monitoring data. However it is infeasible to have experts watch the data all the time. There are devices that can report battery events. These devices sometimes report incorrect event. In order to solve this problem, we propose a time series classification framework to use the expert knowledge to build an accurate classifier, and then use the classifier to monitor the batteries in real time. We first propose an active learning method to efficiently collect the experts' labels for each event. Then we apply various feature extraction methods to convert each time series segment into a feature vector. Finally, we apply random forest classifier to perform the classification. Moreover, in practice, the labeled data is unbalanced, i.e. >99% of the data instances belong to a single label. We use bootstrap to solve this problem. We test our method on a dataset for 500 batteries in 3 months. The results show that our method achieves a very high classification accuracy, using only less than 1% of the dataset as training set.

In this work, we focus on two fundamental questions that are unprecedentedly important to urban planners to understand the functional characteristics of various urban regions throughout a city, namely, (i) how to identify regional weather-traffic sensitivity index throughout a city, that indicates the degree to which the region traffic in a city is impacted by weather changes; (ii) among complex regional features, such as road structure and population density, how to dissect the most influential regional features that drive the urban region traffic to be more vulnerable to weather changes. However, these two questions are nontrivial to answer, because urban traffic changes dynamically over time and is essentially affected by many other factors, which may dominate the overall impact. We make the first study on these questions, by developing a weather-traffic index (WTI) system. The system includes two main components: weather-traffic index establishment and key factor analysis. Using the proposed system, we conducted comprehensive empirical study in Shanghai, and the weather-traffic indices extracted have been validated to be surprisingly consistent with real world observations. Further regional key factor analysis yields interesting results. For example, house age has significant impact on the weather-traffic index, which sheds light on future urban planning and reconstruction.

The impact of inclement weather to urban traffic has been widely observed and studied for many years, with focus primarily on individual road segments by analyzing data from roadside deployed monitors. However, two fundamental questions are still open: (i) how to identify regional weather-traffic sensitivity index throughout a city, that indicates the degree to which the region traffic in a city is impacted by weather changes, (ii) among complex regional features, such as road structure and population density, how to dissect the most influential regional features that drive the urban region traffic to be more vulnerable to weather changes. Answering these questions is unprecedentedly important for urban planners to understand the functional characteristics of various urban regions throughout a city, and to improve traffic prediction and learn the key factors in urban planning. However, these two questions are nontrivial to answer, because urban traffic changes dynamically over time and is essentially affected by many other factors, which may dominate the overall impact. In this work, we make the first study on these questions, by developing a weather-traffic index (WTI) system. The system includes two main components: WTI establishment and key factor analysis. Using the proposed system, we conducted comprehensive empirical study in Shanghai, and the WTI extracted have been validated to be surprisingly consistent with real world observations. Further regional key factor analysis yields interesting results. For example, house age has significant impact on WTI, which sheds light on future urban planning and reconstruction.

The value of large amount of location tracking data has received wide attention in many applications including human behavior analysis, urban transportation planning, and various location-based services (LBS). Nowadays, both scientific and industrial communities are encouraged to collect as much location tracking data as possible, which brings about two issues: 1) it is challenging to process the queries on big location tracking data efficiently, and 2) it is expensive to store several exact data replicas for fault-tolerance. So far, several dedicated storage systems have been proposed to address these issues. However, they do not work well when the query ranges vary widely. In this paper, we present the design of a storage system using diverse replica scheme which improves the query processing efficiency with reduced cost of storage space. To the best of our knowledge, we are the first to investigate the data storage and processing in the context of big location tracking data. Specifically, we conduct in-depth theoretical and empirical analysis of the trade-offs between different spatio-temporal partitioning schemes as well as data encoding schemes. Then we propose an effective approach to select an appropriate set of diverse replicas, which is optimized for the expected query loads while conforming to the given storage space budget. The experiment results confirm that using diverse replicas can significantly improve the overall query performance. The results also demonstrate that the proposed algorithms for the replica selection problem is both effective and efficient.

MapReduce is a programming model designed by Google that has been widely used for both high performance computing and big data processing. Although the programming model is simple, it is very challenging to conduct performance tuning for a MapReduce job, considering the complexities of the configuration parameters and various tradeoffs between the performance gain of the optimization approaches and the extra overhead they bring about. One naive way to address this issue is to run the MapReduce jobs repeatedly using different combinations of configuration parameters and optimization methods, then select the one with the shortest running time. However, real execution is impractical because the combinations may be too many and the time of one run of each combination may be too long. Therefore, it is desirable if we can efficiently estimate the runtime of a job without real execution using only the input data and the configuration parameter settings of the cluster. In this paper, we propose a novel MapReduce simulator called MRTune for runtime estimation of MapReduce jobs. MRTune takes the key distribution of input data into consideration and can work well even when the key distribution of data is skewed. Moreover, MRTune can estimate the runtime of a job in the presence of unpredictable task failures. We evaluate MRTune implementing MapReduce jobs with Zipfian distributed input data. The result shows that MRTune can estimate the runtime of MapReduce jobs with high accuracy and efficiency while the key distribution of input data is skewed. We also conduct two case studies to analyse the impact of data skew and task failures on a MapReduce job.

The rise of GPS-equipped mobile devices has led to the emergence of big trajectory data. In this paper, we study a new path finding query which finds the most frequent path (MFP) during user-specified time periods in large-scale historical trajectory data. We refer to this query as time period-based MFP (TPMFP). Specifically, given a time period T, a source v_s and a destination v_d, TPMFP searches the MFP from v_s to v_d during T. Though there exist several proposals on defining MFP, they only consider a fixed time period. Most importantly, we find that none of them can well reflect people's common sense notion which can be described by three key properties, namely suffix-optimal (i.e., any suffix of an MFP is also an MFP), length-insensitive (i.e., MFP should not favor shorter or longer paths), and bottleneck-free (i.e., MFP should not contain infrequent edges). The TPMFP with the above properties will reveal not only common routing preferences of the past travelers, but also take the time effectiveness into consideration. Therefore, our first task is to give a TPMFP definition that satisfies the above three properties. Then, given the comprehensive TPMFP definition, our next task is to find TPMFP over huge amount of trajectory data efficiently. Particularly, we propose efficient search algorithms together with novel indexes to speed up the processing of TPMFP. To demonstrate both the effectiveness and the efficiency of our approach, we conduct extensive experiments using a real dataset containing over 11 million trajectories.

During the past decade, various GPS-equipped devices have generated a tremendous amount of data with time and location information, which we refer to as big spatio-temporal data. In this paper, we present the design and implementation of CloST, a scalable big spatio-temporal data storage system to support data analytics using Hadoop. The main objective of CloST is to avoid scan the whole dataset when a spatio-temporal range is given. To this end, we propose a novel data model which has special treatments on three core attributes including an object id, a location and a time. Based on this data model, CloST hierarchically partitions data using all core attributes which enables efficient parallel processing of spatio-temporal range scans. According to the data characteristics, we devise a compact storage structure which reduces the storage size by an order of magnitude. In addition, we proposes scalable bulk loading algorithms capable of incrementally adding new data into the system. We conduct our experiments using a very large GPS log dataset and the results show that CloST has fast data loading speed, desirable scalability in query processing, as well as high data compression ratio.

High-dimensional similarity join (HDSJ) is critical for many novel applications in the domain of mobile data management. Nowadays, performing HDSJs efficiently faces two challenges. First, the scale of datasets is increasing rapidly, making parallel computing on a scalable platform a must. Second, the dimensionality of the data can be up to hundreds or even thousands, which brings about the issue of dimensionality curse. In this paper, we address these challenges and study how to perform parallel HDSJs efficiently in the MapReduce paradigm. Particularly, we propose a cost model to demonstrate that it is important to take both communication and computation costs into account as dimensionality and data volume increases. To this end, we propose DAA (Dimension Aggregation Approximation), an efficient compression approach that can help significantly reduce both these costs when performing parallel HDSJs. Moreover, we design DAA-based parallel HDSJ algorithms which can scale up to massive data sizes and very high dimensionality. We perform extensive experiments using both synthetic and real datasets to evaluate the speedup and the scale up of our algorithms.

Many emerging mobile applications require analyzing large spatial datasets. In these applications, efficient query processing relies on spatial access methods such as R-trees. For datasets that are fairly static, R-trees are often built as a data loading process using packing techniques. However, traditional R-tree packing algorithms can only run on a single machine and thereby cannot scale to very large datasets. In this paper, we design and implement a general framework for parallel Rtree packing using MapReduce. This framework sequentially packs each R-tree level from bottom up. For lower levels that have a large number of rectangles, we propose a partition based algorithm for parallel packing. We also discuss two spatial partitioning methods that can efficiently handle heavily skewed datasets. To evaluate the performance, we conducted extensive experiments using large real datasets. The size of the datasets is up to 100GB and the number of spatial objects is up to 2 billion. Besides range queries, k-nearest neighbor searches and spatial joins are also used for evaluation. To the best of our knowledge, it is the first work that evaluates the query performance of packed R-trees on such large datasets with spatial queries other than range queries. The results confirm the scalability of our proposed framework and parallel packing algorithms. It is also shown that our packed R-trees have good query performance and optimal space utilization.

Interference is a critical issue in wireless communications. In a typical multiple-user environment, different users may severely interfere with each other. Coordination among users therefore is an indispensable part for interference management in wireless networks. It is known that coordination among multiple nodes is a costly operation taking a significant amount of valuable communication resource. In this paper, we have an interesting observation that by generating intended patterns, some simultaneous transmissions, i.e., "interference,” can be successfully decoded without degrading the effective throughput in original transmission. As such, an extra and "free” coordination channel can be built. Based on this idea, we propose a DC-MAC to leverage this "free” channel for efficient medium access in a multiple-user wireless network. We theoretically analyze the capacity of this channel under different environments with various modulation schemes. USRP2-based implementation experiments show that compared with the widely adopted CSMA, DC-MAC can improve the channel utilization efficiency by up to 250 percent.

IEEE 802.15.4 standard specifies physical layer (PHY) and medium access control (MAC) sublayer protocols for low-rate and low-power communication applications. In this protocol, every 4-bit symbol is encoded into a sequence of 32 chips that are actually transmitted over the air. The 32 chips as a whole is also called a pseudonoise code (PN-Code). Due to complex channel conditions such as attenuation and interference, the transmitted PN-Code will often be received with some PN-Code chips corrupted. In this paper, we conduct a systematic analysis on these errors occurring at chip level. We find that there are notable error patterns corresponding to different cases. We then show that recognizing these patterns enables us to identify the channel condition in great details. We believe that understanding what happened to the transmission in our way can potentially bring benefit to channel coding, routing, and error correction protocol design. Finally, we propose Simple Rule, a simple yet effective method based on the chip error patterns to infer the link condition with an accuracy of over 96 percent in our evaluations.

RFID has been gaining popularity due to its variety of applications, such as inventory control and localization. One important issue in RFID system is tag identification. In RFID systems, the tag randomly selects a slot to send a Random Number (RN) packet to contend for identification. Collision happens when multiple tags select the same slot, which makes the RN packet undecodable and thus reduces the channel utilization. In this paper, we redesign the RN pattern to make the collided RNs decodable. By leveraging the collision slots, the system performance can be dramatically enhanced. This novel scheme is called DDC, which is able to directly decode the collisions without exact knowledge of collided RNs. In the DDC scheme, we modify the RN generator in RFID tag and add a collision decoding scheme for RFID reader. We implement DDC in GNU Radio and USRP2 based testbed to verify its feasibility. Both theoretical analysis and testbed experiment show that DDC achieves 40 percent tag read rate gain compared with traditional RFID protocol.

Data clustering is an important data mining technology that plays a crucial role in numerous scientific applications. However, it is challenging due to the size of datasets has been growing rapidly to extra-large scale in the real world. Meanwhile, MapReduce is a desirable parallel programming platform that is widely applied in kinds of data process fields. In this paper, we propose an efficient parallel density-based clustering algorithm and implement it by a 4-stages MapReduce paradigm. Furthermore, we adopt a quick partitioning strategy for large scale non-indexed data. We study the metric of merge among bordering partitions and make optimizations on it. At last, we evaluate our work on real large scale datasets using Hadoop platform. Results reveal that the speedup and scale up of our work are very efficient.

RFID has been gaining popularity due to its variety of applications, such as inventory control and localization. One important issue in RFID system is tag identification. In RFID systems, the tag randomly selects a slot to send a Random Number (RN16) packet to contend for identification. Collision happens when multiple tags select the same slot, which makes the RN packet undecodable and thus reduces the channel utilization. In this paper, we redesign the RN pattern to make the collided RNs decodable. By leveraging the collision slots, the system performance can be dramatically enhanced. This novel scheme is called DDC, which is able to directly decode the collisions without exact knowledge of collided RNs. In the DDC scheme, we modify the RN generator in RFID tag and add a collision decoding scheme for RFID reader. We implement DDC in GNU Radio and USRP2 based testbed to verify its feasibility. Both theoretical analysis and testbed experiment show that DDC achieves 40% tag read rate gain compared with traditional RFID protocol.

Recent years have seen an increasing need of wireless networks in a mobile environment serving for more complex tasks and applications. Mobility becomes an indispensable factor of the system design and has been widely recognized as a general cause of packet loss. Though many works have been done on mobility study, to the best of our knowledge, they are mainly based on simulations or analytical studies that assume idealized link conditions. In this work, we experimentally investigate the nature of the error characteristics of mobility-induced packet losses at "chip-level" in IEEE 802.15.4. We believe this more understanding of mobility-induced packet losses can bring great potential benefits for further study on channel coding, routing and protocol design. Toward this end, we design and implement an efficient algorithm to distinguish mobility-induced packet losses from other packet losses of static environments such as attenuation. Our algorithm is greatly advantaged as it needs no training data even when environment changes. Collecting three corrupted packets is sufficient to obtain a satisfactory performance. This feature makes our design in particular suitable for dynamic and mobile environments, allowing real-time mobility induced loss detection in an online manner. Experiments based on GNU Radio testbed show that our algorithm can provide an accuracy of up to 96%.

IEEE 802.15.4 standard specifies physical layer (PHY) and medium access control (MAC)sublayer protocols for low-rate and low-power communication applications. In this protocol, every 4-bit symbol is encoded into a sequence of 32 chips that are actually transmitted over the air. The 32 chips as a whole is also called a pseudo-noise code (PN-Code). Due to complex channel conditions such as attenuation and interference, the transmitted PN-Code will often be received with some PN-Code chips corrupted. In this paper, we conduct a systematic analysis on these errors occurring at chip-level. We find that there are notable error patterns corresponding to different cases. Recognizing these patterns will enable us to identify the channel condition in great details. We believe that understanding what happened to the transmission in our setup can potentially bring benefit to channel coding, routing and error correction protocol design.

Interference is a critical issue in wireless communications. In a typical multiple-user environment, different users may severely interfere with each other. Coordination among users therefore is an indispensable part for interference management in wireless networks. It is known that, coordination among multiple nodes is a costly operation taking a significant amount of valuable communication resource. In this paper, we have an interesting observation that by generating intended patterns, some simultaneous transmissions, i.e., "interference", can be successfully decoded without degrading the effective throughput in original transmission. As such, an extra and "free" coordination channel can be built. Based on this idea we propose a DC-MAC to leverage this "free" channel for efficient medium access in a multiple-user wireless network. We theoretically analyze the capacity of this channel under different environments with various modulation schemes. USRP2-based implementation experiments show that compared with the widely adopted CSMA, DC-MAC can improve the channel utilization efficiency by up to 250%.