Due to the inadequacy of GPS signals in indoor spaces, Indoor Positioning Services (IPSs) have drawn great attention. The popular smartphone localization technique relies on a centralized server to achieve localization, allowing the server to acquire a user's location in fine granularity. To ensure the privacy of IPS users, we propose an Encrypted Indoor Positioning Service (EIPS) model that protects users' privacy from the centralized server and maintains localization accuracy simultaneously. Our EIPS model enables users to encrypt and decrypt their query through an Encryption and Decryption Server (EDS) bi-directionally in a commutative way, so the users' locations remain private to both EIPS and EDS. We also propose Query Split, Artificial Dimensions and Columns to prevent Known Plaintext Attack (KPA). Our analytical and experimental evaluations show that our model is resilient to a variety of privacy attacks without loss of efficiency and accuracy.

This research proposes an advanced approach that improves the efficiency of skyline query processing by significantly reducing the computational cost on object comparisons, i.e., dominance tests between objects. Our solutions are based on two novel concepts. The skyline query over Minimum Bounding Rectangles (MBRs) receives a set of MBRs and returns the MBRs that are not dominated by other MBRs. In the dominance test for MBRs, the detailed attribute values of objects in the MBRs are not accessed. Moreover, the dependent group of MBRs reduces the search space for dominance tests. Objects in an MBR are only compared with the ones in the corresponding dependent groups of the MBR rather than with the entire dataset. Our solutions apply the two concepts to the R-tree in order to use its hierarchical structure in which every node is a natural abstraction of an MBR. Specifically, given the R-tree index of an input dataset, we first eliminate unqualified objects by utilizing the skyline query over MBRs (i.e., intermediate nodes in the R-tree). Subsequently, we generate dependent groups for the skyline MBRs. Two dependent group generation methods that rely on either the sorting technique or the R-tree index are developed. Further, we apply an existing skyline algorithm to every dependent group, and the results of the original skyline query are the union of skyline objects in the dependent groups. In addition, we also analyze the cardinality of the two new concepts based on a probabilistic model, which enables us to analyze the computational complexity of the proposed solutions. Our experimental results show that the proposed solutions are clearly more efficient than the state-of-the-art approaches.

This paper presents an exact solution and a heuristic solution to a UAV-assisted parcel delivery problem, in which UAVs can only be operated in Visual-Line-Of-Sight (VLOS) areas. In our proposed problem, we assume that trucks travel on road networks, and UAVs move in Euclidean spaces and can launch at any locations on roads. We first demonstrate the overview of our exact solution that iterates all permutations of destinations for an optimal delivery route. Given a specific delivery order, an intuitive approach needs to check all possible locations on roads in the VLOS areas and find a globally optimal location for every destination if UAVs are used for delivery. To avoid high computational cost of searching the optimal location at runtime, we propose an advanced index-based alternative, which computes optimal delivery routes in a pre-processing stage. Due to the nature of NP-hard problems, we also propose a heuristic approach that utilizes delivery groups for the proposed problem of practical size. All proposed solutions are evaluated through extensive experiments.

A variety of optimal location problems have been extensively studied in the literature. However, limited visualization systems have been developed for illustrating optimal location selection process. In this demonstration, we present a system that visualizes an advanced solution that can efficiently answer multi-criteria optimal location updating query by incrementally updating the Minimum Overlapping Voronoi Diagram (MOVD) model. Not only does our system display an example as a practical multi-criteria optimal location updating query, but also visualizes the process of the query evaluation in a more intuitive manner. With the object insertion and deletion operations defined over the MOVD model, any object changes in an MOVD can be represented by removing the objects from initial datasets and adding them back with updated attributes. Moreover, Haxe toolkit is used to provide friendly and flexible user interfaces in our system.

This research develops an advanced two-phase MapReduce solution that is able to efficiently address skyline queries on large datasets. Unlike existing parallel skyline approaches, our scheme considers data partitioning, filtering, and parallel skyline evaluation as a holistic query process. In particular, we apply filtering techniques and angle-based partitioning in the first phase, in which unqualified objects are discarded and the processed objects are partitioned by their angles to the origin.In the second phase, local skyline objects in each partition are calculated in parallel, and global skyline objects are output after a merging skyline process. To improve the parallel local skyline calculation, we propose two partition-aware filtering methods that keep skyline candidates in a balanced manner. The aggressive partition-aware filtering aggressively eliminates objects in the partition with the greatest population of candidate objects, whereas the proportional partition-aware filtering slows down the growth of partition population proportionally. Recognizing the lack of studies that incorporate the MapReduce framework into parallel skyline processing, we propose a partialpresort grid-based partition skyline algorithm that is able to significantly improve the merging skyline computation on large datasets. The presort process can be completed in the shuffle phase with little overhead. Our experimental results show the efficiency and effectiveness of the proposed parallel skyline solution utilizing MapReduce on large-scale datasets.

In this demonstration, we present a visualization system offering two advanced solutions that can efficiently address a novel multi-criteria optimal location query by using Overlapping Voronoi Diagrams (OVDs). Our system not only displays an example that applies the advanced solutions to a practical optimal location query, but also visualizes the process of query evaluation by using the solutions, which can help users better understand the two solutions in a more intuitive way. The Real Region as Boundary (RRB) approach calculates real regions of overlapped Voronoi cells, while the Minimum Bounding Rectangle as Boundary (MBRB) approach approximates the cells by their MBRs to simplify region overlapping calculation. For efficient query evaluation and better demonstration, global and local datasets are managed separately and only the local dataset is displayed in a limited visualization area. User interactions are provided in diverse manners in our system by using the Standard Widget Toolkit (SWT) and Open Graphics Library (OpenGL).

This article presents a framework, Frog, for Context-Based File Systems (CBFSs) that aim at simplifying the development of context-based file systems and applications. Unlike existing informed-based context-aware systems, Frog is a unifying informed-based framework that abstracts context-specific solutions as views, allowing applications to make view selections according to application behaviors. The framework can not only eliminate overheads induced by traditional context analysis, but also simplify the interactions between the context-based file systems and applications. Rather than propagating data through solution-specific interfaces, views in Frog can be selected by inserting their names in file path strings. With Frog in place, programmers can migrate an application from one solution to another by switching among views rather than changing programming interfaces. Since the data consistency issues are automatically enforced by the framework, file-system developers can focus their attention on context-specific solutions. We implement two prototypes to demonstrate the strengths and overheads of our design. Inspired by an observation that there are more than 50% of small files (<4KB) in a file system, we create a Bi-context Archiving Virtual File System (BAVFS) that utilizes conservative and aggressive prefetching for the contexts of random and sequential reads. To improve the performance of random read-and-write operations, the Bi-context Hybrid Virtual File System (BHVFS) combines the update-in-place and update-out-of-place solutions for read-intensive and write-intensive contexts. Our experimental results show that the benefits of Frog-based CBFSs outweigh the overheads introduced by integrating multiple context-specific solutions.

This paper presents a novel optimal location selection problem, which can be applied to a wide range of applications.After providing a formal definition of the novel query type, we explore an intuitive approach that sequentially scans all possible object combinations in the search space.Then, we propose an Overlapping Voronoi Diagram (OVD) model that defines OVDs and Minimum OVDs, and construct an algebraic structure under an OVD overlap operation.Based on the OVD model, we design an advanced approach to answer the query.Due to the high complexity of Voronoi diagram overlap computation, we improve the overlap operation by replacing the real boundaries of Voronoi diagrams with their Minimum Bounding Rectangles (MBR).We also propose a cost-bound iterative approach that efficiently processes a large number of Fermat-Weber problems.Our experimental results show that the proposed algorithms can evaluate the novel query type effectively and efficiently.

An increasing number of spatial keyword query techniques that return qualified objects based on a comprehensive consideration of spatial and keyword constraints have been presented in the literature. Due to the complexity of the solutions to spatial keyword queries, systems that can effectively demonstrate the mechanisms will attract interest from the spatial database research community. However, very limited visualization systems have been developed for illustrating spatial keyword query evaluation. In this demonstration, we present a system that visualizes advanced solutions to efficiently answer the Spatial Keyword k Nearest Neighbor (SKkNN) query. With the two-level data management method and the friendly user interface implemented by the Standard Widget Toolkit (SWT) and Open Graphics Library (OpenGL), our system is able to not only interact with users in diverse manners, visualize datasets, and display the SKkNN query evaluation process, but it also helps users better understand the solutions in a more intuitive way.

Numerous geographic information system applications need to retrieve spatial objects which bear user specified keywords close to a given location. In this research, we present efficient approaches to answer spatial keyword queries on spatial networks. In particular, we formally introduce definitions of Spatial Keyword k Nearest Neighbor (SKkNN) and Spatial Keyword Range (SKR) queries. Then, we present a framework of a spatial keyword query evaluation system which is comprised of Keyword Constraint Filter (KCF), Keyword and Spatial Refinement (KSR), and the spatial keyword ranker. KCF employs an inverted index to calculate keyword relevancy of spatial objects, and KSR refines intermediate results by considering both spatial and keyword constraints with the spatial keyword ranker. In addition, we design novel algorithms for evaluating SKkNN and SKR queries. These algorithms employ the inverted index technique, shortest path search algorithms, and network Voronoi diagrams. Our extensive simulations show that the proposed SKkNN and SKR algorithms can answer spatial keyword queries effectively and efficiently.

This paper presents a novel Predictive Energy-Aware Management (PEAM) system that is able to reduce the energy costs of storage systems by appropriately selecting data transmission methods. In particular, we evaluate the energy costs of three methods (1. transfer data without archiving and compression, 2. archive and transfer data, 3. compress and transfer data) in preliminary experiments. According to the results, we observe that the energy consumption of data transmission greatly varies case by case. We cannot simply apply one method in all cases. Therefore, we design an energy prediction model that can estimate the total energy cost of data transmission by using particular transmission methods. Based on the model, our predictive energy-aware management system can automatically select the most energy efficient method for data transmission. Our experimental results show that our system performs better than simply selecting any one among the three methods for data transmission in terms of energy efficiency.

Recognizing that power and cooling cost for data centers are increasing, we address in this study the thermal impact of storage systems. In the first phase of this work, we generate the thermal profile of a storage server containing three hard disks. The profiling results show that disks have comparable thermal impacts as processing and networking elements to overall storage node temperature. We develop a thermal model to estimate the outlet temperature of a storage server based on processor and disk utilizations. The thermal model is validated against data acquired by an infrared thermometer as well as build-in temperature sensors on disks. Next, we apply the thermal model to investigate the thermal impact of workload management on storage systems. Our study suggests that disk-aware thermal management techniques have significant impacts on reducing cooling cost of storage systems. We further show that this work can be extended to analysis the cooling cost of data centers with massive storage capacity.

This paper presents an offloading framework - ORCA - to map I/O-intensive code to a cluster that consists of computing and storage nodes. To reduce data transmission among computing and storage nodes. our offloading framework partitions and schedules CPU-bound and I/O-bound modules to computing nodes and active storage nodes, respectively. From developer's perspective, ORCA helps them to deal with execution-path control, offloading executable code, and data sharing over a network. Powered by the offloading APIs, developers without any I/O offloading or network programming experience are allowed to write new I/O-intensive code running efficiently on clusters. We implement the ORCA framework on a cluster to quantitatively evaluate performance improvements offered by our approach. We run five real-world applications on both homogeneous and heterogeneous computing environments. Experimental results show ORCA speeds up the performance of all the five tested applications by a factor of up to 90.1% with an average of 75.5%. Moreover, the results confirm that ORCA reduces network burden imposed by I/O-intensive applications by a factor of anywhere between 35 to 68.

In this paper, we present an informed prefetching technique called IPODS that makes use of application-disclosed access patterns to prefetch hinted blocks in distributed multi-level storage systems. We develop a prefetching pipeline in IPODS, where an informed prefetching process is divided into a set of independent prefetching steps among multiple storage levels in a distributed system. In the IPODS system, while data blocks are prefetched from hard disks to memory buffers in remote storage servers, data blocks buffered in the servers are prefetched through networks to clients' local cache. We show that these two prefetching steps can be handled in a pipelining manner to improve I/O performance of distributed storage systems. Our IPODS technique differs itself from existing prefetching schemes in two ways. First, IPODS reduces applications' I/O stalls by keeping hinted data in clients' local caches and storage servers' fast buffers (e.g., solid state disks). Second, in a prefetching pipeline, multiple informed prefetching mechanisms semi-dependently coordinate to fetch blocks (1) from low-level (slow) to high-level (fast) storage devices in servers and (2) from high-level devices in servers to clients' local cache. The prefetching pipeline in IPODS judiciously hides network latencies in distributed storage systems, thereby reducing the overall I/O access time in distributed systems. Using a wide range of real-world I/O traces, our experiments show that IPODS can improve noticeably I/O performance of distributed storage systems.

There is a growing demand for large-scale distributed storage systems to support resource sharing and fault tolerance. Although heterogeneity issues of distributed systems have been widely investigated, little attention has yet been paid to security solutions designed for distributed storage systems with heterogeneous vulnerabilities. This fact motivates us to investigate a fragment allocation scheme called S-FAS to improve security of a distributed system where storage sites have a wide variety of vulnerabilities. In the S-FAS approach, we integrate file fragmentation with the secret sharing technique in a distributed storage system with heterogeneous vulnerabilities. Storage sites in a distributed systems are classified into a variety of different server types based on vulnerability characteristics. Given a file and a distributed system, S-FAS allocates fragments of the file to as many different types of nodes as possible in the system. Data confidentiality is preserved because fragments of a file are allocated to multiple storage nodes. We develop storage assurance and dynamic assurance models to evaluate the quality of security offered by S-FAS. Analysis results show that fragment allocations made by S-FAS lead to enhanced security because of the consideration of heterogeneous vulnerabilities in distributed storage systems.