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BackgroundThe importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. ObjectiveThe objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. MethodsActigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). ResultsDeep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional logistic regression. “CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional logistic regression (0.6463). ConclusionsDeep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep.

In relational database management systems, views supplement basic query constructs to cope with the demand for "higher-level" views of data. Moreover, in traditional query optimization, answering a query using a set of existing materialized views can yield a more efficient query execution plan. Due to their effectiveness, views are attractive to data stream management systems. In order to support views over streams, a data stream management system should employ a closed (or composable) continuous query language. A closed query language is a language in which query inputs and outputs are interpreted in the same way, hence allowing query composition. This article introduces the Synchronized SQL (or SyncSQL) query language that defines a data stream as a sequence of modify operations against a relation. SyncSQL enables query composition through the unified interpretation of query inputs and outputs. An important issue in continuous queries over data streams is the frequency by which the answer gets refreshed and the conditions that trigger the refresh. Coarser periodic refresh requirements are typically expressed as sliding windows. In this article, the sliding window approach is generalized by introducing the synchronization principle that empowers SyncSQL with a formal mechanism to express queries with arbitrary refresh conditions. After introducing the semantics and syntax, we lay the algebraic foundation for SyncSQL and propose a query-matching algorithm for deciding containment of SyncSQL expressions. Then, the article introduces the Nile-SyncSQL prototype to support SyncSQL queries. Nile-SyncSQL employs a pipelined incremental evaluation paradigm in which the query pipeline consists of a set of differential operators. A cost model is developed to estimate the cost of SyncSQL query execution pipelines and to choose the best execution plan from a set of different plans for the same query. An experimental study is conducted to evaluate the performance of Nile-SyncSQL. The experimental results illustrate the effectiveness of Nile-SyncSQL and the significant performance gains when views are enabled in data stream management systems. Categories and Subject Descriptors: H.2.3 [Database Management]: Languages--Query language General Terms: Language, Experimentation, Performance Additional Key Words and Phrases: Data streams, query language, expression matching, incremental evaluation, views ACM Reference Format: Ghanem, T. M., Elmagarmid, A. K., Larson, P.-A., and Aref, W. G. 2010. Supporting views in data stream management systems. ACM Trans. Datab. Syst, 35, 1, Article 1 (February 2010), 47 pages. DOI = 10.1145/1670243.1670244 http://doi.acm.org/10.1145/1670243.1670244

Often, in the real world, entities have two or more representations in databases. Duplicate records do not share a common key and/or they contain errors that make duplicate matching a difficult task. Errors are introduced as the result of transcription errors, incomplete information, lack of standard formats, or any combination of these factors. In this paper, we present a thorough analysis of the literature on duplicate record detection. We cover similarity metrics that are commonly used to detect similar field entries, and we present an extensive set of duplicate detection algorithms that can detect approximately duplicate records in a database. We also cover multiple techniques for improving the efficiency and scalability of approximate duplicate detection algorithms. We conclude with coverage of existing tools and with a brief discussion of the big open problems in the area. Index Terms--Duplicate detection, data cleaning, data integration, record linkage, data deduplication, instance identification, database hardening, name matching, identity uncertainty, entity resolution, fuzzy duplicate detection, entity matching.

Two research efforts have been conducted to realize sliding-window queries in data stream management systems, namely, query reevaluation and incremental evaluation. In the query reevaluation method, two consecutive windows are processed independently of each other. On the other hand, in the incremental evaluation method, the query answer for a window is obtained incrementally from the answer of the preceding window. In this paper, we focus on the incremental evaluation method. Two approaches have been adopted for the incremental evaluation of sliding-window queries, namely, the input-triggered approach and the negative tuples approach. In the input-triggered approach, only the newly inserted tuples flow in the query pipeline and tuple expiration is based on the timestamps of the newly inserted tuples. On the other hand, in the negative tuples approach, tuple expiration is separated from tuple insertion where a tuple flows in the pipeline for every inserted or expired tuple. The negative tuples approach avoids the unpredictable output delays that result from the input-triggered approach. However, negative tuples double the number of tuples through the query pipeline, thus reducing the pipeline bandwidth. Based on a detailed study of the incremental evaluation pipeline, we classify the incremental query operators into two classes according to whether an operator can avoid the processing of negative tuples or not. Based on this classification, we present several optimization techniques over the negative tuples approach that aim to reduce the overhead of processing negative tuples while avoiding the output delay of the query answer. A detailed experimental study, based on a prototype system implementation, shows the performance gains over the input-triggered approach of the negative tuples approach when accompanied with the proposed optimizations. Index Terms--Data stream management systems, pipelined query execution, negative tuples.

Rank-aware query processing has emerged as a key requirement in modern applications. In these applications, efficient and adaptive evaluation of top-k queries is an integral part of the application semantics. In this article, we introduce a rank-aware query optimization framework that fully integrates rank-join operators into relational query engines. The framework is based on extending the System R dynamic programming algorithm in both enumeration and pruning. We define ranking as an interesting physical property that triggers the generation of rank-aware query plans. Unlike traditional join operators, optimizing for rank-join operators depends on estimating the input cardinality of these operators. We introduce a probabilistic model for estimating the input cardinality, and hence the cost of a rank-join operator. To our knowledge, this is the first effort in estimating the needed input size for optimal rank aggregation algorithms. Costing ranking plans is key to the full integration of rank-join operators in real-world query processing engines. Since optimal execution strategies picked by static query optimizers lose their optimality due to estimation errors and unexpected changes in the computing environment, we introduce several adaptive execution strategies for top-k queries that respond to these unexpected changes and costing errors. Our reactive reoptimization techniques change the execution plan at runtime to significantly enhance the performance of running queries. Since top-k query plans are usually pipelined and maintain a complex ranking state, altering the execution strategy of a running ranking query is an important and challenging task. We conduct an extensive experimental study to evaluate the performance of the proposed framework. The experimental results are twofold: (1) we show the effectiveness of our cost-based approach of integrating ranking plans in dynamic programming cost-based optimizers; and (2) we show a significant speedup (up to 300%) when using our adaptive execution of ranking plans over the state-of-the-art mid-query reoptimization strategies. Categories and Subject Descriptors: H.2.4 [Database Management]: Systems General Terms: Algorithms, Design, Experimentation, Performance Additional Key Words and Phrases: Advanced query processing, ranking, top-k, adaptive processing, rank-aware optimization

Periodicity mining is used for predicting trends in time series data. Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. Existing periodicity mining algorithms assume that the periodicity rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in O(n log n) time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered periodicity rates and periodic patterns. Real-data experiments demonstrate the practicality of the discovered periodic patterns. Index Terms--Periodic patterns mining, temporal data mining, time series forecasting, time series analysis.

Advances in the media and entertainment industries, including streaming audio and digital TV, present new challenges for managing and accessing large audio-visual collections. Current content management systems support retrieval using low-level features, such as motion, color, and texture. However, low-level features often have little meaning for naive users, who much prefer to identify content using high-level semantics or concepts. This creates a gap between systems and their users that must be bridged for these systems to be used effectively. To this end, in this paper, we first present a knowledge-based video indexing and content management framework for domain specific videos (using basketball video as an example). We will provide a solution to explore video knowledge by mining associations from video data. The explicit definitions and evaluation measures (e.g., temporal support and confidence) for video associations are proposed by integrating the distinct feature of video data. Our approach uses video processing techniques to find visual and audio cues (e.g., court field, camera motion activities, and applause), introduces multilevel sequential association mining to explore associations among the audio and visual cues, classifies the associations by assigning each of them with a class label, and uses their appearances in the video to construct video indices. Our experimental results demonstrate the performance of the proposed approach. Index Terms--Video mining, multimedia systems, database management, knowledge-based systems.

A novel framework is proposed to enhance the concept-oriented indexing of video databases. This framework includes a semantic-sensitive video content representation framework, semantic video concept interpretation, a novel semantic video-classifier training framework, and a concept-oriented video database organization technique.

Large repositories of data contain sensitive information that must be protected against unauthorized access. The protection of the confidentiality of this information has been a long-term goal for the database security research community and for the government statistical agencies. Recent advances in data mining and machine learning algorithms have increased the disclosure risks that one may encounter when releasing data to outside parties. A key problem, and still not sufficiently investigated, is the need to balance the confidentiality of the disclosed data with the legitimate needs of the data users. Every disclosure limitation method affects, in some way, and modifies true data values and relationships. In this paper, we investigate confidentiality issues of a broad category of rules, the association rules. In particular, we present three strategies and five algorithms for hiding a group of association rules, which is characterized as sensitive. One rule is characterized as sensitive if its disclosure risk is above a certain privacy threshold. Sometimes, sensitive rules should not be disclosed to the public since, among other things, they may be used for inferring sensitive data, or they may provide business competitors with an advantage. We also perform an evaluation study of the hiding algorithms in order to analyze their time complexity and the impact that they have in the original database. Index Terms--Privacy preserving data mining, association rule mining, sensitive rule hiding.

Mining of periodic patterns in time-series databases is an interesting data mining problem. It can be envisioned as a tool for forecasting and prediction of the future behavior of time-series data. Incremental mining refers to the issue of maintaining the discovered patterns over time in the presence of more items being added into the database. Because of the mostly append only nature of updating time-series data, incremental mining would be very effective and efficient. Several algorithms for incremental mining of partial periodic patterns in time-series databases are proposed and are analyzed empirically. The new algorithms allow for online adaptation of the thresholds in order to produce interactive mining of partial periodic patterns. The storage overhead of the incremental online mining algorithms is analyzed. Results show that the storage overhead for storing the intermediate data structures pays off as the incremental online mining of partial periodic patterns proves to be significantly more efficient than the nonincremental nononline versions. Moreover, a new problem, termed merge mining, is introduced as a generalization of incremental mining. Merge mining can be defined as merging the discovered patterns of two or more databases that are mined independently of each other. An algorithm for merge mining of partial periodic patterns in time-series databases is proposed and analyzed. Index Terms--Data mining, time-series databases, incremental mining, online mining.

In this paper, we address the problem of cache invalidation in mobile and wireless client/server environments. We present cache invalidation techniques that can scale not only to a large number of mobile clients, but also to a large number of data items that can be cached in the mobile clients. We propose two scalable algorithms: the Multidimensional Bit-Sequence (MD-BS) algorithm and the Multilevel Bit-Sequence (ML-BS) algorithm. Both algorithms are based on our prior work on the Basic Bit-Sequences (BS) algorithm. Our study shows that the proposed algorithms are effective for a large number of cached data items with low update rates. The study also illustrates that the algorithms can be used with other complementary techniques to address the problem of cache invalidation for data items with varied update and access rates. Index Terms--Mobile data access, cache invalidation, disconnected operation, data broadcast.

This paper describes the integration of a multidatabase system and a knowledge-base system to support the data-integration component of a Data Warehouse. The multidatabase system integrates various component databases with a common query language; however, it does not provide capability for schema integration and other utilities necessary for Data Warehousing. In addition, the knowledge base system offers a declarative logic language with second-order syntax but first-order semantics for integrating the schemes of the data sources into the warehouse and for defining complex, recursively defined materialized views. Furthermore, deductive rules are also used for cleaning, checking the integrity and summarizing the data imported into the Data Warehouse. The Knowledge Base System features an efficient incremental view maintenance mechanism that is used for refreshing the Data Warehouse, without querying the data sources. Index Terms--Multidatabase, schema integration, data warehouse, materialized view, knowledge base system, deductive rule, and active rule.

Content-based video database access control is becoming very important, but it depends on the progresses of the following related research issues: (a) efficient video analysis for supporting semantic visual concept representation; (b) effective video database indexing structure; (c) the development of suitable video database models; and (d) the development of access control models tailored to the characteristics of video data. In this paper, we propose a novel approach to support multilevel access control in video databases. Our access control technique combines a video database indexing mechanism with a hierarchical organization of visual concepts (i.e., video database indexing units), so that different classes of users can access different video elements or even the same video element with different quality levels according to their permissions. These video elements, which, in our access control mechanism, are used for specifying the authorization objects, can be a semantic cluster, a subcluster, a video scene, a video shot, a video frame, or even a salient object (i.e., region of interest). In the paper, we first introduce our techniques for obtaining these multilevel video access units. We also propose a hierarchical video database indexing technique to support our multilevel video access control mechanism. Then, we present an innovative access control model which is able to support flexible multilevel access control to video elements. Moreover, the application of our multilevel video database modeling, representation, and indexing for MPEG-7 is discussed. Categories and Subject Descriptors: I.2.10 [Artificial Intelligence]: Vision and Scene Understanding--Video analysis; D.4.6 [Operating Systems]: Security and Protection--Access control; H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing--Indexing methods General Terms: Security Additional Key Words and Phrases: Video database models, access control, indexing schemes

A new method for automatic image segmentation is proposed. Application of the method to automatic face detection is discussed.

In this paper we develop a formal basis for research into the reliability aspects of transaction processing in multidatabase systems. We define a new correctness notion called global committability (GC) for the correct unilateral commit and retry recovery of global transactions in an autonomous MDBS environment. This notion facilitates to ensure the isolation property of global transactions when the retry approach is applied. The formalization work illustrates that conventional serializability (SR) notion and recoverability (RC) notion are not sufficient to specify a correct execution (i.e., isolated execution and recovery) of global transactions when the unilateral commit and the retry recovery are used to ensure the atomicity of global transactions. This work is significant because the unilateral commit and the retry recovery is an attractive complementary means to the undo recovery (whose correct schedule is specified by the conventional RC notion) for advanced transaction applications with characteristics of site autonomy and long-lived execution. Index Terms - Multidatabases, transaction management, global transactions, local transactions, unilateral commit, retry recovery, serializability, recoverability, global committability.

This paper describes E-DEVICE, an extensible active knowledge base system (KBS) that supports the processing of event-driven, production, and deductive rules into the same active OODB system. E-DEVICE provides the infrastructure for the smooth integration of various declarative rule typos, such as production and deductive rules, into an active OODB system that supports low-level event-driven rules only by: 1) mapping each declarative rule into one event-driven rule, offering centralized rule selection control for correct run-time behavior and conflict resolution, and 2) using complex events to map the conditions of declarative rules and monitor the database to incrementally match those conditions. E-DEVICE provides the infrastructure for easily extending the system by adding: 1) new rule typos as subtypes of existing ones, and 2) transparent optimizations to the rule matching network. The resulting system is a flexible, yet efficient, KBS that gives the user the ability to express knowledge in a variety of high-level forms for advanced problem solving in data intensive applications. Index Terms--Knowledge base system, production rules, deductive rules, derived attributes, aggregation, negation, active object-oriented database.

1. INTRODUCTION Advances in wireless networking technology and portable information appliances have engendered a new paradigm of computing, called mobile computing, in which users who carry portable devices have access […], Recent advances in wireless data networking and portable information appliances have engendered a new paradigm of computing, called mobile computing, in which users carrying portable devices have access to data and information services regardless of their physical location or movement behavior. In the meantime, research addressing information access in mobile environments has proliferated. In this survey, we provide a concrete framework and categorization of the various ways of supporting mobile client-server computing for information access. We examine characteristics of mobility that distinguish mobile client-server computing from its traditional counterpart. We provide a comprehensive analysis of new paradigms and enabler concepts for mobile client-server computing, including mobile-aware adaptation, extended client-server model, and mobile data access. A comparative and detailed review of major research prototypes for mobile information access is also presented. Categories and Subject Descriptors: C.2.4 [Computer-Communication Networks]: Distributed Systems General Terms: Algorithms, Design Additional Key Words and Phrases: Application adaptation, cache invalidation, caching, client/server, data dissemination, disconnected operation, mobile applications, mobile client/server, mobile computing, mobile data, mobility awareness, survey, system adaptation

Background: The explosion of consumer electronics and social media are facilitating the rise of the Quantified Self (QS) movement where millions of users are tracking various aspects of their daily life using social media, mobile technology, and wearable devices. Data from mobile phones, wearables and social media can facilitate a better understanding of the health behaviors of individuals. At the same time, there is an unprecedented increase in childhood obesity rates worldwide. This is a cause for grave concern due to its potential long-term health consequences (e.g., diabetes or cardiovascular diseases). Childhood obesity is highly prevalent in Qatar and the Gulf Region. In this study we examine the feasibility of capturing quantified-self data from social media, wearables and mobiles within a weight lost camp for overweight children in Qatar.Methods: Over 50 children (9-12 years old) and parents used a wide range of technologies, including wearable sensors (actigraphy), mobile and social media (WhatsApp and Instagram) to collect data related to physical activity and food, that was then integrated with physiological data to gain insights about their health habits. In this paper, we report about the acquired data and visualization techniques following the 360° Quantified Self (360QS) methodology (Haddadi et al., ICHI 587-92, 2015).Results: 360QS allows for capturing insights on the behavioral patterns of children and serves as a mechanism to reinforce education of their mothers via social media. We also identified human factors, such as gender and cultural acceptability aspects that can affect the implementation of this technology beyond a feasibility study. Furthermore, technical challenges regarding the visualization and integration of heterogeneous and sparse data sets are described in the paper.Conclusions: We proved the feasibility of using 360QS in childhood obesity through this pilot study. However, in order to fully implement the 360QS technology careful planning and integration in the health professionals' workflow is needed.Trial Registration: The trial where this study took place is registered at ClinicalTrials.gov on 14 November 2016 ( NCT02972164 ). [ABSTRACT FROM AUTHOR]

This paper describes the design and implementation of a web-based video database system (WVTDB) that demonstrates our research on video data modeling, semantic content-based video query, and video database system architecture. The video data model of WVTDB is based on multilevel video data abstractions and annotation layering, thus allowing dynamic and incremental video annotation and indexing, multiuser view sharing, and video data reuse. Users can query, retrieve, and browse video data based on their semantic content descriptions and temporal constraints on the video segments. WVTDB employs a modular system architecture that supports distributed video query processing and subquery caching. Several techniques, such as video wrappers and lazy delivery, are also proposed specifically to address the network bandwidth limitations for this kind of web-based system. We also address adaptivity, data access control, and user profile issues. Index Terms - Video database, video query, video retrieval, video indexing, video browsing, video representation, query cache, World Wide Web, access control.