Latent sector errors can cause problems, such as data loss and performance degradation, in a storage system. It is essential to identify failure disks for storage reliability. At the same time, in order to improve storage efficiency, it is necessary to dynamically adjust the redundancy settings (such as erasure coding parameters) and disk scrubbing rate (regularly scan and repair sector errors) according to the health level of different brands or types of disks. However, current work studies these two aspects separately without considering their interactions in a unified system. Moreover, balancing the conflicting objectives of increasing reliability and cost is a challenge. In this work, we propose a deep learning model for health prediction and a reinforcement learning model for strategy adjustment, to solve the above problems. The health prediction part trains a health prediction model for disks of different brands, which outputs the health level of each disk and calculates the overall health levels of different brands. The strategy adjustment part employs a threshold-based reinforcement learning framework Th-DQN to dynamically adjust the redundancy settings and scrubbing rate of disks from this particular brand, in accordance with the brand's overall health level. The experimental results on the datasets of different brands show that our proposed method can select suitable strategies according to different health levels, while satisfying the target reliability requirement and minimizing the system cost.

Positioning data and other sensor measurements, such as camera orientation, have become important contextual features generated by mobile devices during video recording, which proved to be increasingly beneficial to video search. To enable access to videos based on their metadata (e.g., geo-properties produced by GPS and digital compass), a model representing camera field of view (FOV) is needed. Vector model of previous work, which simplifies FOV by ignoring the viewable angle for search efficiency, has become popular in georeferenced video search. However, when the viewable angle is large, many false positives and false negatives occur, which are undesirable for filtering of georeferenced video search. This paper proposes a new model, which can appropriately represent the actual FOV as a filtering step, without any false positive or false negative. Based on this model, we investigate how to process five types of overlap queries for searching videos as spatio-temporal objects. To verify the effectiveness of our model and the corresponding query processing algorithms, experiments on a real data set we collected and a large synthetic data set are conducted. The results show that the proposed model can perform much better compared with the existing vector model, and the accuracy of its search results remains almost steady, even when the angle changes.

With the advance of various location-acquisition technologies, a myriad of GPS trajectories can be collected every day. However, the raw coordinate data captured by sensors often cannot reflect real positions due to many physical constraints and some rules of law. How to accurately match GPS trajectories to roads on a digital map is an important issue. Many existing methods still cannot meet stringent performance requirements, especially for low/unstable sampling rate and noisy/lost data. As in practice, some other measurements such as speed and moving direction are collected together with the spatial locations acquired, we can make use of not only location coordinates but all data collected. In this paper, we propose a novel model using the related meta-information to describe a moving object, and present an algorithm called IF-Matching for map-matching. It can handle many ambiguous cases which cannot be correctly matched by existing methods. We run our algorithm with taxi trajectory data on a city-wide road network. Compared with two state-of-the-art algorithms of ST-Matching and the winner of GIS Cup 2012, our approach achieves more accurate results.

Locality sensitive hashing (LSH) and its variants are widely used for approximate kNN (k nearest neighbor) search in high-dimensional space. The success of these techniques largely depends on the ability of preserving kNN information. Unfortunately, LSH only provides a high probability that nearby points in the original space are projected into nearby region in a new space. This potentially makes many false positives and false negatives resulting from unrelated points. Many extensions of LSH aim to alleviate the above issue by improving the distance preserving ability. In this paper, we abound improving LSH function but propose a novel idea to enhance the performance by transforming the original data to a new space before applying LSH. A preserving-ignoring transformation (PIT) function satisfying some rigorous conditions can be used to convert original points to an interim space with strict distance preserving-ignoring capacity. Based on this property, a linear order is utilized to build an efficient index structure in the interim space. Finally, LSH can be applied to candidate set searched by our index structure for final results. Experiments are conducted and the proposed approach performs better than state-of-the-art methods SK-LSH, DSH and NSH in terms of both accuracy and efficiency.

Travel route planning aims to mine user's attributes and recommend personalized routes. How to build interest model for users and understand their real intention brings great challenges. This paper presents an approach which mines the user interest model by multi-source social media (e.g., travelogues and check-in records), and understands the user's real intention by active behavior such as point of interest (POI) inputs. In order to unify heterogeneous data from different sources, a topical package is built as the measurement space. Based on the topical package, user topical package is modeled to find user interest and route topical package is constructed to describe the attributes of each route. User's active behavior can also be considered during route planning, where top ranked routes are finally recommended. The proposed multi-source topical package (MSTP) approach is evaluated on a real dataset and compared with two state-of-the-art methods. The result shows that MSTP performs better for providing personalized travel routes.

With the advance of various location-acquisition technologies, a myriad of GPS trajectories can be collected every day. However, the raw coordinate data captured by sensors often cannot reflect real positions due to many physical constraints and some rules of law. How to accurately match GPS trajectories to roads on a digital map is an important issue. The problem of map-matching is fundamental for many applications. Unfortunately, many existing methods still cannot meet stringent performance requirements in engineering. In particular, low/unstable sampling rate and noisy/lost data are usually big challenges. Information fusion of different data sources is becoming increasingly promising nowadays. As in practice, some other measurements such as speed and moving direction are collected together with the spatial locations acquired, we can make use of not only location coordinates but all data collected. In this paper, we propose a novel model using the related meta-information to describe a moving object, and present an algorithm called IF-Matching for map-matching. It can handle many ambiguous cases which cannot be correctly matched by existing methods. We run our algorithm with taxi trajectory data on a city-wide road network. Compared with two state-of-the-art algorithms of ST-Matching and the winner of GIS Cup 2012, our approach achieves more accurate results.

As positioning data and other sensor information such as orientation measurement became powerful contextual features generated by mobile devices during video recording, a model capturing geographic field-of-view (FOV) has been developed for georeferenced video search. The accurate representation of an FOV is through the geometric shape of a circular sector. However, previous work simply employed a rectilinear vector model to represent the coverage area of a video scene. In this study, we propose to use a novel circular sector model with beginning-ending vectors for FOV representation which additionally explores viewable angle information. Its major advantage is that it leads to a more accurate georeferenced video search without false positives or false negatives (which occur in previous model using single vector). We demonstrate how our model can be applied to perform different types of overlap queries for spatial data selection in a unified framework, while providing competitive performance in terms of efficiency.