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.

Many distributed systems use a replication mechanism for reliability and availability. On the other hand, application developers have to consider minimum consistency requirement for each application. Therefore, a replication protocol that supports multiple consistency models is required. Multi-Consistency Data Replication (McRep) is a proxy-based replication protocol and can support multiple consistency models. However, McRep has a potential problem in that a replicator relaying all request and reply messages between clients and replicas can be a performance bottleneck and a Single-Point-of-Failure (SPoF). In this paper, we introduce the multi-consistency support mechanism of McRep to a combined state-machine and deferred-update replication protocol to eliminate the performance bottleneck and SPoF. The state-machine and deferred-update protocols are well-established approaches for fault-tolerant data management systems. But each method can ensure only a specific consistency model. Thus, we adaptively select a replication method from the two replication bases. In our protocol, the functionality of the McRep's replicator is realized by clients and replicas. Each replica has new roles in serialization of all transactions and managing all views of the database, and each client has a new role in managing status of its transactions. We have implemented and evaluated the proposed protocol and compared to McRep. The evaluation results show that the proposed protocol achieved comparable throughput of transactions to McRep. Especially the proposed protocol improved the throughput up to 16% at a read-heavy workload in One-Copy. Finally, we demonstrated the proposed failover mechanism. As a result, a failure of a leader replica did not affect continuity of the entire replication system unlike McRep.

Technological developments in wireless communication have led to an increasing demand for radio frequencies. This has necessitated the practice of spectrum sharing to ensure optimal usage of the limited frequencies, provided this does not cause interference. This paper presents a framework for managing an unexpected situation in which a primary user experiences harmful interference with regard to database-driven secondary use of spectrum allocated to the primary user towards 5G mobile networks, where the primary user is assumed to be a radar system. In our proposed framework, the primary user informs a database that they are experiencing harmful interference. Receiving the information, the database updates a primary exclusive region in which secondary users are unable to operate in the licensed spectrum. Subsequent to the update, this primary exclusive region depends on the knowledge about the secondary users when the primary user experiences harmful interference, knowledge of which is stored in the database. We assume a circular primary exclusive region centered at a primary receiver and derive an optimal radius of the primary exclusive region by applying stochastic geometry. Then, for each type of knowledge stored in the database for the secondary user, we evaluate the optimal radius for a target probability that the primary user experiences harmful interference. The results show that the more detailed the knowledge of the secondary user's density and transmission power stored in the database, the smaller the radius that has to be determined for the primary exclusive region after the update and the more efficient the spatial reuse of the licensed spectrum that can be achieved.

Given a set of positive-weighted points and a query rectangle r (specified by a client) of given extents, the goal of a maximizing range sum (MaxRS) query is to find the optimal location of r such that the total weights of all points covered by r is maximized. In this paper, we address the problem of processing MaxRS queries over road network databases and propose two new external memory methods. Through a set of simulations, we evaluate the performance of the proposed methods.

In this paper, we exploit MapReduce framework and other optimizations to improve the performance of hash join algorithms on multi-core CPUs, including No partition hash join and partition hash join. We first implement hash join algorithms with a shared-memory MapReduce model on multi-core CPUs, including partition phase, build phase, and probe phase. Then we design an improved cuckoo hash table for our hash join, which consists of a cuckoo hash table and a chained hash table. Based on our implementation, we also propose two optimizations, one for the usage of SIMD instructions, and the other for partition phase. Through experimental result and analysis, we finally find that the partition hash join often outperforms the No partition hash join, and our hash join algorithm is faster than previous work by an average of 30%.

It is crucial to promote interdisciplinary research and recommend collaborators from different research fields via academic database analysis. This paper addresses a problem to characterize researchers' interests with a set of diverse research topics found in a large-scale academic database. Specifically, we first use latent Dirichlet allocation to extract topics as distributions over words from a training dataset. Then, we convert the textual features of a researcher's publications to topic vectors, and calculate the centroid of these vectors to summarize the researcher's interest as a single vector. In experiments conducted on CiNii Articles, which is the largest academic database in Japan, we show that the extracted topics reflect the diversity of the research fields in the database. The experiment results also indicate the applicability of the proposed topic representation to the author disambiguation problem.

In recent years, society has experienced several changes in its ways and methods of consuming. Nowadays, the diversity and the customization of products and services have provoked that the consumer needs continuously change. Hence, the database systems support e-business processes are required to be timeliness and adaptable to the changing preferences. Autonomous Decentralized Database System (ADDS), has been proposed in order to satisfy the enhanced requirements of current on-line e-business applications. Autonomy and decentralization of subsystems help to achieve short response times in highly competitive situations and an autonomous Coordination Mobile Agent (CMA) has been proposed to achieve flexibility in a highly dynamic environment. However, a problem in ADDS is as the number of sites increases, the distribution and harmonization of product information among the sites are turning difficult. Therefore, many users cannot be satisfied quickly. As a result, system timeliness is inadequate. To solve this problem, a self configuration technology is proposed. This technology can configure the system to the evolving situation dynamically for achieving high response. A simulation shows the effectiveness of the proposed technology in a large-scale system. Finally, an implementation of this technology is presented.

In this paper, we propose a communication-efficient top-k continuous query processing method on distributed local nodes where data are horizontally partitioned. A designated coordinator server takes the role of issuing queries from users to local nodes and delivering the results to users. The final results are requested via a top-k subscription which lets local nodes know which data and updates need to be returned to users. Our proposed method makes use of the active previously posed queries to identify a small set of needed top-k subscriptions. In addition, with the pre-indexed nodes' skylines, the number of local nodes to be subscribed can be significantly reduced. As a result, only a small number of subscriptions are informed to a small number of local nodes resulting in lower communication overhead. Furthermore, according to dynamic data updates, we also propose a method that prevents nodes from reporting needless updates and also maintenance procedures to preserve the consistency. The results of experiments that measure the volume of transferred data show that our proposed method significantly outperforms the previously proposed methods.

This paper shows accuracy of using azimuth-variable path-loss fitting in white-space (WS) boundary-estimation. We perform experiments to evaluate this method, and demonstrate that the required number of sensors can be significantly reduced. We have proposed a WS boundary-estimation framework that utilizes sensors to not only obtain spectrum sensing data, but also to estimate the boundaries of the incumbent radio system (IRS) coverage. The framework utilizes the transmitter position information and pathloss fitting. The pathloss fitting describes the IRS coverage by approximating the well-known pathloss prediction formula from the received signal power data, which is measured using sensors, and sensor-transmitter separation distances. To enhance its accuracy, we have further proposed a pathloss-fitting method that employs azimuth variables to reflect the azimuth dependency of the IRS coverage, including the antenna directivity of the transmitter and propagation characteristics.

In many movies, social conditions and awareness of the issues of the times are depicted in any form. Even if fantasy and science fiction are works far from reality, the character relationship does mirror the real world. Therefore, we try to understand social conditions of the real world by analyzing the movie. As a way to analyze the movies, we propose a method of estimating interpersonal relationships of the characters, using a machine learning technique called Markov Logic Network (MLN) from movie script databases on the Web. The MLN is a probabilistic logic network that can describe the relationships between characters, which are not necessarily satisfied on every line. In experiments, we confirmed that our proposed method can estimate favors between the characters in a movie with F-measure of 58.7%. Finally, by comparing the relationships with social indicators, we discussed the relevance of the movies to the real world.

In this paper, we propose the novel concept of a spectrum database for improving the efficiency of spectrum utilization. In the current design of TV white space spectrum databases, a propagation model is utilized to determine the spectrum availability. However, this propagation model has poor accuracy for radio environment estimation because it requires a large interference margin for the PU coverage area to ensure protection of primary users (PUs); thus, it decreases the spectrum sharing efficiency. The proposed spectrum database consists of radio environment measurement results from sensors on mobile terminals such as vehicles and smart phones. In the proposed database, actual measurements of radio signals are used to estimate radio information regarding PUs. Because the sensors on mobile terminals can gather a large amount of data, accurate propagation information can be obtained, including information regarding propagation loss and shadowing. In this paper, we first introduce the architecture of the proposed spectrum database. Then, we present experimental results for the database construction using actual TV broadcast signals. Additionally, from the evaluation results, we discuss the extent to which the proposed database can mitigate the excess interference margin.

Order-Preserving Encryption (OPE) is an encryption system that preserves the numerical (or alphabetical) order of plaintexts. Since many operations of communications and databases are based on order relations of data (plaintexts), OPE is regarded as a core technique for various applications. However, the intrinsic property of OPE makes constructing an efficient and secure OPE system hard to achieve. In particular, OPE can guarantee only limited security compared to general encryption systems. Only a few results on OPE systems were presented, and no theoretical research based on provable security has been presented until recently. Lately, some approaches have been suggested. However, every known result is unsatisfactory for formalizing the provable security of OPE systems. In this paper, we propose a new OPE system that employs only a simple pseudorandom number function, and therefore has an advantage for practical usage.

Breast cancer is a serious disease across the world, and it is one of the largest causes of cancer death for women. The traditional diagnosis is not only time consuming but also easily affected. Hence, artificial intelligence (AI), especially neural networks, has been widely used to assist to detect cancer. However, in recent years, the computational ability of a neuron has attracted more and more attention. The main computational capacity of a neuron is located in the dendrites. In this paper, a novel neuron model with dendritic nonlinearity (NMDN) is proposed to classify breast cancer in the Wisconsin Breast Cancer Database (WBCD). In NMDN, the dendrites possess nonlinearity when realizing the excitatory synapses, inhibitory synapses, constant-1 synapses and constant-0 synapses instead of being simply weighted. Furthermore, the nonlinear interaction among the synapses on a dendrite is defined as a product of the synaptic inputs. The soma adds all of the products of the branches to produce an output. A back-propagation-based learning algorithm is introduced to train the NMDN. The performance of the NMDN is compared with classic back propagation neural networks (BPNNs). Simulation results indicate that NMDN possesses superior capability in terms of the accuracy, convergence rate, stability and area under the ROC curve (AUC). Moreover, regarding ROC, for continuum values, the existing 0-connections branches after evolving can be eliminated from the dendrite morphology to release computational load, but with no influence on the performance of classification. The results disclose that the computational ability of the neuron has been undervalued, and the proposed NMDN can be an interesting choice for medical researchers in further research.

In this letter, we propose a secrecy criterion for outsourcing encrypted databases. In encrypted databases, encryption schemes revealing some information are often used in order to manipulate encrypted data efficiently. The proposed criterion is based on inference analysis for databases: We simulate attacker's inference on specified secret information with and without the revealed information from the encrypted database. When the two inference results are the same, then secrecy of the specified information is preserved against outsourcing the encrypted database. We also show that the proposed criterion is decidable under a practical setting.

Accessing a geo-location database is one of the approaches for a secondary user (SU) to obtain the list of available channels for its operation. Channel availability is calculated based on information stored in the geo-location database and information submitted by the SU so that primary users (PU) are protected from harmful interference. The available channel checking process is modeled as a number of intersection tests between the protected contours of PUs and the operation area of the SU regarding to all potential channels. Existing studies indicated that these intersection tests consume time and introduce overhead to the database, especially when the contours or the operation areas are represented by n-polygons and the number of vertices n is a large number. This paper presents a novel method of determining available channels which reduces the number of intersection tests. By submitting SU's preferred channels or the number of channels to be checked to the database, the calculation time and database's load will be reduced significantly. This paper also presents analysis and simulation results of the database workload and the average number of channels obtained per query on different query methods. Suitable query method can be selected based on the number of similar channels in neighbor areas and the maximum number of intersection tests.

The current development of video quality assessment algorithms suffers from the lack of available video sequences for training, verification and validation to determine and enhance the algorithm's application scope. The Joint Effort Group of the Video Quality Experts Group (VQEG-JEG) is currently driving efforts towards the creation of large scale, reproducible, and easy to use databases. These databases will contain bitstreams of recent video encoders (H.264, H.265), packet loss impairment patterns and impaired bitstreams, pre-parsed bitstream information into files in XML syntax, and well-known objective video quality measurement outputs. The database is continuously updated and enlarged using reproducible processing chains. Currently, more than 70,000 sequences are available for statistical analysis of video quality measurement algorithms. New research questions are posed as the database is designed to verify and validate models on a very large scale, testing and validating various scopes of applications, while subjective assessment has to be limited to a comparably small subset of the database. Special focus is given on the principles guiding the database development, and some results are given to illustrate the practical usefulness of such a database with respect to the detailed new research questions.

This paper propose a novel method for obtaining statistical results such as averages, variances, and correlations without leaking any raw data values from data-holders by using multiple pseudonyms. At present, to obtain statistical results using a large amount of data, we need to collect all data in the same storage device. However, gathering real-world data that were generated by different people is not easy because they often contain private information. The authors split the roles of servers into publishing pseudonyms and collecting answers. Splitting these roles, different entities can more easily join as pseudonym servers than in previous secure multi-party computation methods and there is less chance of collusion between servers. Thus, our method enables data holders to protect themselves against malicious attacks from data users. We also estimated a typical problem that occurred with our method and added a pseudonym availability confirmation protocol to prevent the problem. We report our evaluation of the effectiveness of our method through implementation and experimentation and discuss how we incorporated the WebSocket protocol and MySQL Memoty Storage Engine to remove the bottleneck and improve the implementation style. Finally, we explain how our method can obtain averages, variances, and correlation from 5000 data holders within 50 seconds.

A top-k keyword query in relational databases returns k trees of tuples — where the tuples containing the query keywords are connected via primary key-foreign key relationships — in the order of relevance to the query. Existing works are classified into two categories: 1) the schema-based approach and 2) the schema-free approach. We focus on the former utilizing database schema information for more effective ranking of the query results. Ranking measures used in existing works can be classified into two categories: 1) the size of the tree (i.e., the syntactic score) and 2) ranking measures, such as TF-IDF, borrowed from the information retrieval field. However, these measures do not take into account semantic relevancy among relations containing the tuples in the query results. In this paper, we propose a new ranking method that ranks the query results by utilizing semantic relevancy among relations containing the tuples at the schema level. First, we propose a structure of semantically strongly related relations, which we call the strongly related tree (SRT). An SRT is a tree that maximally connects relations based on the lossless join property. Next, we propose a new ranking method, SRT-Rank, that ranks the query results by a new scoring function augmenting existing ones with the concept of the SRT. SRT-Rank is the first research effort that applies semantic relevancy among relations to ranking the results of keyword queries. To show the effectiveness of SRT-Rank, we perform experiments on synthetic and real datasets by augmenting the representative existing methods with SRT-Rank. Experimental results show that, compared with existing methods, SRT-Rank improves performance in terms of four quality measures — the mean normalized discounted cumulative gain (nDCG), the number of queries whose top-1 result is relevant to the query, the mean reciprocal rank, and the mean average precision — by up to 46.9%, 160.0%, 61.7%, and 63.8%, respectively. In addition, we show that the query performance of SRT-Rank is comparable to or better than those of existing methods.

Information Security Management Systems (ISMSs) play important roles in helping organizations to manage their information securely. However, establishing, managing, and maintaining ISMSs is not an easy task for most organizations because an ISMS has many participants and tasks, and requires many kinds of documents. Therefore, organizations with ISMSs demand tools that can support them to perform all tasks in ISMS lifecycle processes consistently and continuously. To realize such support tools, a database system that manages ISO/IEC 27000 series, which are international standards for ISMSs, and ISMS documents, which are the products of tasks in ISMS lifecycle processes, is indispensable. The database system should manage data of the standards and documents for all available versions and translations, relationship among the standards and documents, authorization to access the standards and documents, and metadata of the standards and documents. No such database system has existed until now. This paper presents an information security management database system (ISMDS) that manages ISO/IEC 27000 series and ISMS documents. ISMDS is a meta-database system that manages several databases of standards and documents. ISMDS is used by participants in ISMS as well as tools supporting the participants to perform tasks in ISMS lifecycle processes. The users or tools can retrieve data from all versions and translations of the standards and documents. The paper also presents some use cases to show the effectiveness of ISMDS.

To better support data-intensive workflows which are typically built out of various independently developed executables, this paper proposes extensions to parallel database systems called User-Defined eXecutables (UDX) and collective queries. UDX facilitates the description of workflows by enabling seamless integrations of external executables into SQL statements without any efforts to write programs confirming to strict specifications of databases. A collective query is an SQL query whose results are distributed to multiple clients and then processed by them in parallel, using arbitrary UDX. It provides efficient parallelization of executables through the data transfer optimization algorithms that distribute query results to multiple clients, taking both communication cost and computational loads into account. We implement this concept in a system called ParaLite, a parallel database system based on a popular lightweight database SQLite. Our experiments show that ParaLite has several times higher performance over Hive for typical SQL tasks and has 10x speedup compared to a commercial DBMS for executables. In addition, this paper studies a real-world text processing workflow and builds it on top of ParaLite, Hadoop, Hive and general files. Our experiences indicate that ParaLite outperforms other systems in both productivity and performance for the workflow.