Cloud computing has become the mainstream computing paradigm nowadays. More and more data owners (DO) choose to outsource their data to a cloud service provider (CSP), who is responsible for data management and query processing on behalf of DO, so as to cut down operational costs for the DO.  However, in real-world applications, CSP may be untrusted, hence it is necessary to authenticate the query result returned from the CSP.  In this paper, we consider the problem of approximate string query result authentication in the context of database outsourcing. Based on Merkle Hash Tree (MHT) and Trie, we propose an authenticated tree structure named MTrie for authenticating approximate string query results. We design efficient algorithms for query processing and query result authentication. To verify effectiveness of our method, we have conducted extensive experiments on real datasets and the results show that our proposed method can effectively authenticate approximate string query results.

Order-preserving encryption using the hypergeomatric probability distribution leaks about the half bits of a plaintext and the distance between two arbitrary plaintexts. To solve these problems, Popa et al. proposed a mutable order-preserving encoding. This is a keyless encoding scheme that adopts an order-preserving index locating the corresponding ciphertext via tree-based data structures. Unfortunately, it has the following shortcomings. First, the frequency of the ciphertexts reveals that of the plaintexts. Second, the indices are highly correlated to the corresponding plaintexts. For these reasons, statistical cryptanalysis may identify the encrypted fields using public information. To overcome these limitations, we propose a multi-tree approach to the mutable order-preserving encoding. The cost of interactions increases by the increased number of trees, but the proposed scheme mitigates the distribution leakage of plaintexts and also reduces the problematic correlation to plaintexts.

Parallel processing is a typical approach to answer analytical queries on large database. As the size of the database increases, we often try to increase the parallelism by incorporating more processing nodes. However, this approach increases the possibility of node failure as well. According to the conventional practice, if a failure occurs during query processing, the database system restarts the query processing from the beginning. Such temporal cost may be unacceptable to the user. This paper proposes a fault-tolerant query processing mechanism, named PhoeniQ, for analytical parallel database systems. PhoeniQ continuously takes a checkpoint for every operator pipeline and replicates the output of each stateful operator among different processing nodes. If a single processing node fails during query processing, another can promptly take over the processing. Hence, PhoneniQ allows the database system to efficiently resume query processing after a partial failure event. This paper presents a key design of PhoeniQ and prototype-based experiments to demonstrate that PhoeniQ imposes negligible performance overhead and efficiently continues query processing in the face of node failure.

In this letter, we propose a novel transferable sparse regression (TSR) method, for cross-database facial expression recognition (FER). In TSR, we firstly present a novel regression function to regress the data into a latent representation space instead of a strict binary label space. To further alleviate the influence of outliers and overfitting, we impose a row sparsity constraint on the regression term. And a pairwise relation term is introduced to guide the feature transfer learning. Secondly, we design a global graph to transfer knowledge, which can well preserve the cross-database manifold structure. Moreover, we introduce a low-rank constraint on the graph regularization term to uncover additional structural information. Finally, several experiments are conducted on three popular facial expression databases, and the results validate that the proposed TSR method is superior to other non-deep and deep transfer learning methods.

In infrastructure-as-a-service platforms, cloud users can adjust their database (DB) service scale to dynamic workloads by changing the number of virtual machines running a DB management system (DBMS), called DBMS instances. Replicating a DBMS instance is a non-trivial task since DBMS replication is time-consuming due to the trend that cloud vendors offer high-spec DBMS instances. This paper presents BalenaDB, which performs urgent DBMS replication for handling sudden workload increases. Unlike convectional replication schemes that implicitly assume DBMS replicas are generated on remote machines, BalenaDB generates a warmed-up DBMS replica on an instance running on the local machine where the master DBMS instance runs, by leveraging the master DBMS resources. We prototyped BalenaDB on MySQL 5.6.21, Linux 3.17.2, and Xen 4.4.1. The experimental results show that the time for generating the warmed-up DBMS replica instance on BalenaDB is up to 30× shorter than an existing DBMS instance replication scheme, achieving significantly efficient memory utilization.

This paper proposes a measurement-based spectrum database (MSD) with clustered fading distributions toward greater storage efficiencies. The conventional MSD can accurately model the actual characteristics of multipath fading by plotting the histogram of instantaneous measurement data for each space-separated mesh and utilizing it in communication designs. However, if the database contains all of a distribution for each location, the amount of data stored will be extremely large. Because the main purpose of the MSD is to improve spectral efficiency, it is necessary to reduce the amount of data stored while maintaining quality. The proposed method reduces the amount of stored data by estimating the distribution of the instantaneous received signal power at each point and integrating similar distributions through clustering. Numerical results show that clustering techniques can reduce the amount of data while maintaining the accuracy of the MSD. We then apply the proposed method to the outage probability prediction for the instantaneous received signal power. It is revealed that the prediction accuracy is maintained even when the amount of data is reduced.

The need for real-time use of human dynamics data is increasing. The technical requirements for this include improved databases for handling a large amount of data as well as highly accurate sensing of people's movements. A bitmap index format has been proposed for high-speed processing of data that spreads in a two-dimensional space. Using the same format is expected to provide a service that searches queries, reads out desired data, visualizes it, and analyzes it. In this study, we propose a coding format that enables human dynamics data to compress it in the target data size, in order to save data storage for successive increase of real-time human dynamics data. In the proposed method, the spatial population distribution, which is expressed by a probability distribution, is approximated and compressed using the one-pixel one-byte data format normally used for image coding. We utilize two kinds of approximation, which are accuracy of probability and precision of spatial location, in order to control the data size and the amount of information. For accuracy of probability, we propose a non-linear mapping method for the spatial distribution, and for precision of spatial location, we propose spatial scalable layered coding to refine the mesh level of the spatial distribution. Also, in order to enable additional detailed analysis, we propose another scalable layered coding that improves the accuracy of the distribution. We demonstrate through experiments that the proposed data approximation and coding format achieve sufficient approximation of spatial population distribution in the given condition of target data size.

In this letter we analyze the economic worth of GPU on analytical processing of GPU-accelerated database management systems (DBMSes). To this end, we conducted rigorous experiments with TPC-H across three popular GPU DBMSes. Consequently, we show that co-processing with CPU and GPU in the GPU DBMSes was cost-effective despite exposed concerns.

Over the past several years, the research of micro-expression recognition (MER) has become an active topic in affective computing and computer vision because of its potential value in many application fields, e.g., lie detection. However, most previous works assumed an ideal scenario that both training and testing samples belong to the same micro-expression database, which is easily broken in practice. In this letter, we hence consider a more challenging scenario that the training and testing samples come from different micro-expression databases and investigated unsupervised cross-database MER in which the source database is labeled while the label information of target database is entirely unseen. To solve this interesting problem, we propose an effective method called target-adapted least-squares regression (TALSR). The basic idea of TALSR is to learn a regression coefficient matrix based on the source samples and their provided label information and also enable this learned regression coefficient matrix to suit the target micro-expression database. We are thus able to use the learned regression coefficient matrix to predict the micro-expression categories of the target micro-expression samples. Extensive experiments on CASME II and SMIC micro-expression databases are conducted to evaluate the proposed TALSR. The experimental results show that our TALSR has better performance than lots of recent well-performing domain adaptation methods in dealing with unsupervised cross-database MER tasks.

Spectrum databases are required to assist the process of radio propagation estimation for spectrum sharing. Especially, a measurement-based spectrum database achieves highly efficient spectrum sharing by storing the observed radio environment information such as the signal power transmitted from a primary user. However, when the average received signal power is calculated in a given square mesh, the bias of the observation locations within the mesh strongly degrades the accuracy of the statistics because of the influence of terrain and buildings. This paper proposes a method for determining the statistics by using mesh clustering. The proposed method clusters the feature vectors of the measured data by using the k-means and Gaussian mixture model methods. Simulation results show that the proposed method can decrease the error between the measured value and the statistically processed value even if only a small amount of data is available in the spectrum database.

The main purpose of Codemark is to distribute digital contents using offline media. Due to the main purpose of Codemark, Codemark cannot be used on digital images. It has high robustness on only printed images. This paper presents a new color code called Robust Index Code (RIC for short), which has high robustness on JPEG Compression and Resize targeting digital images. RIC embeds a remote database index to digital images so that users can reach to any digital contents. Experimental results, using our implemented RIC encoder and decoder, have shown high robustness on JPEG Comp. and Resize of the proposed codemark. The embedded database indexes can be extracted 100% on compressed images to 30%. In conclusion, it is able to store all the type of digital products by embedding indexes into digital images to access database, which means it makes a Superdistribution system with digital images realized. Therefore RIC has the potential for new Internet image services, since all the images encoded by RIC are possible to access original products anywhere.

In this paper, we establish a novel bimodal emotion database from physiological signals and facial expression, which is named as PSFE. The physiological signals and facial expression of the PSFE database are respectively recorded by the equipment of the BIOPAC MP 150 and the Kinect for Windows in the meantime. The PSFE database altogether records 32 subjects which include 11 women and 21 man, and their age distribution is from 20 to 25. Moreover, the PSFE database records three basic emotion classes containing calmness, happiness and sadness, which respectively correspond to the neutral, positive and negative emotion state. The general sample number of the PSFE database is 288 and each emotion class contains 96 samples.

Many concurrency control protocols for B-trees use latch-coupling because its execution is efficient on a single machine. Some studies have indicated that latch-coupling may involve a performance bottleneck when using multicore processors in a shared-everything environment, but no studies have considered the possible performance bottleneck caused by sending messages between processing elements (PEs) in shared-nothing environments. We propose two new concurrency control protocols, “LCFB” and “LCFB-link”, which require no latch-coupling in optimistic processes. The LCFB-link also innovates B-link approach within each PE to reduce the cost of modifications in the PE, as a solution to the difficulty of consistency management for the side pointers in a parallel B-tree. The B-link algorithm is well known as a protocol without latch-coupling, but B-link has the difficulty of guaranteeing the consistency of the side pointers in a parallel B-tree. Experimental results in various environments indicated that the system throughput of the proposed protocols was always superior to those of the conventional protocols, particularly in large-scale configurations, and using LCFB-link was effective for higher update ratios. In addition, to mitigate access skew, data should migrate between PEs. We have demonstrated that our protocols always improve the system throughput and are effective as concurrency controls for data migration.

In this study, toward the development of smartphone-based monitoring system for life logging, we collect over 1,400 hours of data by recording including both the outdoor and indoor daily activities of 19 subjects, under practical conditions with a smartphone and a small camera. We then construct a huge human activity database which consists of an environmental sound signal, triaxial acceleration signals and manually annotated activity tags. Using our constructed database, we evaluate the activity recognition performance of deep neural networks (DNNs), which have achieved great performance in various fields, and apply DNN-based adaptation techniques to improve the performance with only a small amount of subject-specific training data. We experimentally demonstrate that; 1) the use of multi-modal signal, including environmental sound and triaxial acceleration signals with a DNN is effective for the improvement of activity recognition performance, 2) the DNN can discriminate specified activities from a mixture of ambiguous activities, and 3) DNN-based adaptation methods are effective even if only a small amount of subject-specific training data is available.

Query autocompletion is an important and practical technique when users want to search for desirable information. As mobile devices become more and more popular, one of the main applications is location-aware service, such as Web mapping. In this paper, we propose a new solution to location-aware query autocompletion. We devise a trie-based index structure and integrate spatial information into trie nodes. Our method is able to answer both range and top-k queries. In addition, we discuss the extension of our method to support the error tolerant feature in case user's queries contain typographical errors. Experiments on real datasets show that the proposed method outperforms existing methods in terms of query processing performance.

As the role of wireless communication is becoming more important for realizing a future connected society for not only humans but also things, spectrum scarcity is becoming severe, because of the huge numbers of mobile terminals and many types of applications in use. In order to realize sustainable wireless connection under limited spectrum resources in a future wireless world, a new dynamic spectrum management scheme should be developed that considers the surrounding radio environment and user preferences. In this paper, we discuss a new spectrum utilization framework for a future wireless world called the “smart spectrum.” There are four main issues related to realizing the smart spectrum. First, in order to recognize the spectrum environment accurately, spectrum measurement is an important technology. Second, spectrum modeling for estimating the spectrum usage and the spectrum environment by using measurement results is required for designing wireless parameters for dynamic spectrum use in a shared spectrum environment. Third, in order to effectively gather the measurement results and provide the spectrum information to users, a measurement-based spectrum database can be used. Finally, smart spectrum management that operates in combination with a spectrum database is required for realizing efficient and organized dynamic spectrum utilization. In this paper, we discuss the concept of the smart spectrum, fundamental research studies of the smart spectrum, and the direction of development of the smart spectrum for targeting the future wireless world.

The development of smart devices has led to the growth of Location-Based Social Networking Services (LBSNSs). In this paper, we introduce an l-Close Range Friends query that finds all l-hop friends of a user within a specified range. We also propose a query processing method on Social Grid Index (SGI). Using real datasets, the performance of our method is evaluated.

Most existing methods on research collaborator recommendation focus on promoting collaboration within a specific discipline and exploit a network structure derived from co-authorship or co-citation information. To find collaboration opportunities outside researchers' own fields of expertise and beyond their social network, we present an interdisciplinary collaborator recommendation method based on research content similarity. In the proposed method, we calculate textual features that reflect a researcher's interests using a research grant database. To find the most relevant researchers who work in other fields, we compare constructing a pairwise similarity matrix in a feature space and exploiting existing social networks with content-based similarity. We present a case study at the Graduate University for Advanced Studies in Japan in which actual collaborations across departments are used as ground truth. The results indicate that our content-based approach can accurately predict interdisciplinary collaboration compared with the conventional collaboration network-based approaches.

In Twitter-like services, countless messages are being posted in real-time every second all around the world. Timely knowledge about what kinds of information are diffusing in social media is quite important. For example, in emergency situations such as earthquakes, users provide instant information on their situation through social media. The collective intelligence of social media is useful as a means of information detection complementary to conventional observation. We have developed a system for monitoring and analyzing information diffusion data in real-time by tracking retweeted tweets. A tweet retweeted by many users indicates that they find the content interesting and impactful. Analysts who use this system can find tweets retweeted by many users and identify the key people who are retweeted frequently by many users or who have retweeted tweets about particular topics. However, bursting situations occur when thousands of social media messages are suddenly posted simultaneously, and the lack of machine resources to handle such situations lowers the system's query performance. Since our system is designed to be used interactively in real-time by many analysts, waiting more than one second for a query results is simply not acceptable. To maintain an acceptable query performance, we propose a capacity control method for filtering incoming tweets using extra attribute information from tweets themselves. Conventionally, there is a trade-off between the query performance and the accuracy of the analysis results. We show that the query performance is improved by our proposed method and that our method is better than the existing methods in terms of maintaining query accuracy.

Similarity search is an important and fundamental problem, and thus widely used in various fields of computer science including multimedia, computer vision, database, information retrieval, etc. Recently, since loitering behavior often leads to abnormal situations, such as pickpocketing and terrorist attacks, its analysis attracts increasing attention from research communities. In this paper, we present AntiLoiter, a loitering discovery system adopting efficient similarity search on surveillance videos. As we know, most of existing systems for loitering analysis, mainly focus on how to detect or identify loiterers by behavior tracking techniques. However, the difficulties of tracking-based methods are known as that their analysis results are heavily influenced by occlusions, overlaps, and shadows. Moreover, tracking-based methods need to track the human appearance continuously. Therefore, existing methods are not readily applied to real-world surveillance cameras due to the appearance discontinuity of criminal loiterers. To solve this problem, we abandon the tracking method, instead, propose AntiLoiter to efficiently discover loiterers based on their frequent appearance patterns in longtime multiple surveillance videos. In AntiLoiter, we propose a novel data structure Luigi that indexes data using only similarity value returned by a corresponding function (e.g., face matching). Luigi is adopted to perform efficient similarity search to realize loitering discovery. We conducted extensive experiments on both synthetic and real surveillance videos to evaluate the efficiency and efficacy of our approach. The experimental results show that our system can find out loitering candidates correctly and outperforms existing method by 100 times in terms of runtime.