Context-aware Query Research Articles

Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design

Integrating Internet of Things (IoT) sensor data from heterogeneous sources with domain knowledge and context information in real-time is a challenging task in IoT healthcare data management applications that can be solved with semantics. Existing IoT platforms often have issues with preserving the privacy of patient data. Moreover, configuring and managing context-aware stream processing queries in semantic IoT platforms requires much manual, labor-intensive effort. Generic queries can deal with context changes but often lead to performance issues caused by the need for expressive real-time semantic reasoning. In addition, query window parameters are part of the manual configuration and cannot be made context-dependent. To tackle these problems, this paper presents DIVIDE, a component for a semantic IoT platform that adaptively derives and manages the queries of the platform’s stream processing components in a context-aware and scalable manner, and that enables privacy by design. By performing semantic reasoning to derive the queries when context changes are observed, their real-time evaluation does require any reasoning. The results of an evaluation on a homecare monitoring use case demonstrate how activity detection queries derived with DIVIDE can be evaluated in on average less than 3.7 seconds and can therefore successfully run on low-end IoT devices.

Context-Aware Querying, Geolocalization, and Rephotography of Historical Newspaper Images

Newspapers contain a wealth of historical information in the form of articles and illustrations. Libraries and cultural heritage institutions have been digitizing their collections for decades to enable web-based access to and retrieval of information. A number of challenges arise when dealing with digitized collections, such as those of KBR, the Royal Library of Brussels (used in this study), which contain only page-level metadata, making it difficult to extract information from specific contexts. A context-aware search relies heavily on metadata enhancement. Therefore, when using metadata at the page level, it is even more challenging to geolocalize less-known landmarks. To overcome this challenge, we have developed a pipeline for geolocalization and visualization of historical photographs. The first step of this pipeline consists of converting page-level metadata to article-level metadata. In the next step, all articles with building images were classified based on image classification algorithms. Moreover, to correctly geolocalize historical photographs, we propose a hybrid approach that uses both textual metadata and image features. We conclude this research paper by addressing the challenge of visualizing historical content in a way that adds value to humanities research. It is noteworthy that a number of historical urban scenes are visualized using rephotography, which is notoriously challenging to get right. This study serves as an important step towards enriching historical metadata and facilitating cross-collection linkages, geolocalization, and the visualization of historical newspaper images. Furthermore, the proposed methodology is generic and can be used to process untagged photographs from social media, including Flickr and Instagram.

Enhancing Clinical Information Retrieval through Context-Aware Queries and Indices.

The big data revolution has created a hefty demand for searching large-scale electronic health records (EHRs) to support clinical practice, research, and administration. Despite the volume of data involved, fast and accurate identification of clinical narratives pertinent to a clinical case being seen by any given provider is crucial for decision-making at the point of care. In the general domain, this capability is accomplished through a combination of the inverted index data structure, horizontal scaling, and information retrieval (IR) scoring algorithms. These technologies are also being used in the clinical domain, but have met limited success, particularly as clinical cases become more complex. One barrier affecting clinical performance is that contextual information, such as negation, temporality, and the subject of clinical mentions, impact clinical relevance but is not considered in general IR methodologies. In this study, we implemented a solution by identifying and incorporating the aforementioned semantic contexts as part of IR indexing/scoring with Elasticsearch. Experiments were conducted in comparison to baseline approaches with respect to: 1) evaluation of the impact on the quality (relevance) of the returned results, and 2) evaluation of the impact on execution time and storage requirements. The results showed a 5.1-23.1% improvement in retrieval quality, along with achieving 35% faster query execution time. Cost-wise, the solution required 1.5-2 times larger space and about 3 times increase in indexing time. The higher relevance demonstrated the merit of incorporating contextual information into clinical IR, and the near-constant increase in time and space suggested promising scalability.

Context-Aware Query Selection for Active Learning in Event Recognition.

Activity recognition is a challenging problem with many practical applications. In addition to the visual features, recent approaches have benefited from the use of context, e.g., inter-relationships among the activities and objects. However, these approaches require data to be labeled, entirely available beforehand, and not designed to be updated continuously, which make them unsuitable for surveillance applications. In contrast, we propose a continuous-learning framework for context-aware activity recognition from unlabeled video, which has two distinct advantages over existing methods. First, it employs a novel active-learning technique that not only exploits the informativeness of the individual activities but also utilizes their contextual information during query selection; this leads to significant reduction in expensive manual annotation effort. Second, the learned models can be adapted online as more data is available. We formulate a conditional random field model that encodes the context and devise an information-theoretic approach that utilizes entropy and mutual information of the nodes to compute the set of most informative queries, which are labeled by a human. These labels are combined with graphical inference techniques for incremental updates. We provide a theoretical formulation of the active learning framework with an analytic solution. Experiments on six challenging datasets demonstrate that our framework achieves superior performance with significantly less manual labeling.

IOT SİSTEMLERİ İÇİN VERİTABANI SİSTEM ÖNERİLERİ

Internet of Things (IoT) is an interconnection of different types of information assets in which data is continuously generated and transmitted over the Internet. Technologies of the sensor, RFID, GPS, mobile devices, and Internet-enabled actuators play a significant role in IoT systems. IoT brings out new challenges in terms of data and information management because it is not easy to collect and manage a large amount of heterogeneous data that is aggregated at very high velocity as well as to retrieve and manage the information that is hidden within this large volume of data. In this paper, I discuss the main factors affecting the efficiency of data management in IoT systems, specifically query processing and transaction management. There are many lessons learned from traditional database systems, distributed systems, and sensor networks, however, traditional solutions are often inadequate to meet the needs of applications in such a complex ecosystem, namely IoT. In traditional database systems, for instance, query operations are usually local, and execution costs depend on the current processor power and other resource constraints (i.e. memory). On the other hand, transaction management mechanisms guarantee the ACID properties in order to provide overall data integrity. It is apparent that different types of IOT applications that operate on heterogeneous, streaming, real-time, and geographically distributed large data will significantly change the well-known aspects of querying and transaction management. Context-aware querying, distributed querying, MapReduce computing model and flexible transaction models such as web-based transaction handling are some of the current issues discussed in this paper. With the succinct yet comprehensive information presented in this work, I intend to provide a guide for researchers in the IoT systems, especially in the context of database systems.

Predicting Failing Queries in Video Search

The ability to predict when a video search query is not likely to deliver satisfying search results is expected to enable more effective search results optimizations and improved search experience for users. In this paper, we propose a novel context-aware query failure prediction approach that predicts whether a particular query submitted in a user's search session is likely to fail. The approach builds on the well-known concept of query performance prediction introduced in conventional text-based Web search to estimate the query's retrieval performance, but extends this concept with two novel characteristics, user indicators and engine indicators. User indicators are derived from transaction logs, capture the patterns of user interactions with the video search engine, and exploit the context in which a particular query was submitted. Engine indicators are derived from the search results list and measure the consistency of visual search results at the level of visual concepts and textual metadata associated with videos. Extensive evaluation of the approach on a test set containing over one million video search queries shows its effectiveness and demonstrates a significant improvement over traditional and state-of-the-art baseline approaches.

Context-aware Distributed Complex Event Processing Method for Event Cloud in Internet of Things

With the rapid development of Internet of Things (IoT), enormous events are produced by various kinds of devices at high speed. Complex Event Processing (CEP) is the key part of IoT middleware which can help the user to get semantic meanings of primitive events. Context-awareness is an important feature of CEP engine. In this paper a high performance distributed context-aware CEP architecture and method is proposed for internet of things. Context is modeled as fuzzy ontology to support uncertainty and linguistic variables in event queries. Based on fuzzy ontology query and similarity based distributed reasoning, complex event query plans are generated and context-aware queries are rewritten into context independent sub-queries. Data window is partitioned according to different event patterns and context. The sub-queries are optimized and executed parallel based on data partition. The experiments show that this method can support fuzzy context in CEP and have acceptable performance and scalability.

When music makes a scene

Music frequently occurs as an important reinforcing and meaning-creating element in multimodal human experiences. This way, cross-modal connotative associations are established, which are actively exploited in professional multimedia productions. A lay user who wants to use music in a similar way may have a result in mind, but may lack the right musical vocabulary to express the corresponding information need. However, if the connotative associations between music and visual narrative are strong enough, characterizations of music in terms of a narrative multimedia context can be envisioned. In this article, we present the outcomes of a user study considering this problem. Through a survey for which respondents were recruited via crowdsourcing methods, we solicited descriptions of cinematic situations for which fragments of royalty-free production music would be suitable soundtracks. As we will show, these descriptions can reliably be recognized by other respondents as belonging to the music fragments that triggered them. We do not fix any description vocabulary beforehand, but rather give respondents a lot of freedom to express their associations. From these free descriptions, common narrative elements emerge that can be generalized in terms of event structure. The insights gained this way can be used to inform new conceptual foundations for supervised methods, and to provide new perspectives on meaningful and multimedia context-aware querying, retrieval and analysis.

The Research on CEP Based on Query Rewriting for CPS

Now we are entering the era of Big Data, and the important data source of which is Cyber Physical System (CPS) data, those data can hardly be processed by present data processing technology. Complex Event Processing (CEP) is the key part of middleware of CPS, which can process the big data, and context-awareness is an important feature of CEP engine. There are some challenges in context-aware complex event processing for CPS, such as heterogeneous, massive data, distributed event processing, uncertainly; therefore, we propose a distributed context-aware event processing method, based on context representation proposed fuzzy ontology and a distributed multi-level context reasoning method to support uncertainty and linguistic variables in event queries. Based on fuzzy ontology query and similarity based distributed reasoning, complex event query plans are generated and context-aware queries are rewritten into context independent sub-queries. Data window is partitioned according to different event patterns and context. The sub-queries are optimized and executed parallel based on data partition. The experiments show that this method can support fuzzy context in CEP and have acceptable performance and scalability.

Mining Concept Sequences from Large-Scale Search Logs for Context-Aware Query Suggestion

Query suggestion plays an important role in improving usability of search engines. Although some recently proposed methods provide query suggestions by mining query patterns from search logs, none of them models the immediately preceding queries as context systematically, and uses context information effectively in query suggestions. Context-aware query suggestion is challenging in both modeling context and scaling up query suggestion using context. In this article, we propose a novel context-aware query suggestion approach. To tackle the challenges, our approach consists of two stages. In the first, offline model-learning stage , to address data sparseness, queries are summarized into concepts by clustering a click-through bipartite. A concept sequence suffix tree is then constructed from session data as a context-aware query suggestion model. In the second, online query suggestion stage , a user’s search context is captured by mapping the query sequence submitted by the user to a sequence of concepts. By looking up the context in the concept sequence suffix tree, we suggest to the user context-aware queries. We test our approach on large-scale search logs of a commercial search engine containing 4.0 billion Web queries, 5.9 billion clicks, and 1.87 billion search sessions. The experimental results clearly show that our approach outperforms three baseline methods in both coverage and quality of suggestions.

CareDB

We demonstrate CareDB , a context and preference-aware database system. CareDB provides scalable personalized location-based services to users based on their preferences and current surrounding context. Unlike existing location-based database systems that answer queries based solely on proximity in distance, CareDB considers user preferences and various types of context in determining the answer to location-based queries. To this end, CareDB does not aim to define new location-based queries, instead, it aims to redefine the answer of existing location-based queries. To achieve its goals, CareDB has several distinguishing characteristics that revolve around a generic and extensible preference and context-aware query processing framework that addresses (a) scalable, efficient preference joins, (b) gracefully handling contextual attributes that are expensive to derive, and (c) support for uncertain attributes.

A Web-Based and Mobile Environmental Management System

This paper describes a Web-based and mobile system specifically developed to monitor fish species at risk. Such a system integrates spatial functionality to allow users not only to visualise maps and metadata of the area of concern but also to perform context-aware queries and updating of spatial datasets. The spatial datasets are provided by the Canadian Department of Fisheries and Oceans (DFO) and the prototype is customised to the specific needs of the Great Lakes Laboratory for Fisheries and Aquatic Sciences (GLLFAS) Fish Habitat Section requirements for fish species at risk assessment. Currently, researchers, habitat biologists and enforcement officers have access to the fisheries database, containing layers of biological information solely from the office. Delivering these data overlaid on base maps of the Great Lakes region to a GPS-enabled hand-held device and linking it to each task currently being investigated allows for mobile DFO biologists and enforcement officers in the field to make informed decisions immediately. In this paper we describe the system and demonstrate how it is used by the DFO in practice.

CONQUER: A Methodology for Context-Aware Query Processing on the World Wide Web

A major impediment to accurate information retrieval from the World Wide Web is the inability of search engines to incorporate semantics in the search process. This research presents a methodology, CONQUER (CONtext-aware QUERy processing), that enhances the semantic content of Web queries using two complementary knowledge sources: lexicons and ontologies. The methodology constructs a semantic net using the original query as a seed, and refines the net with terms from the two knowledge sources. The enhanced query, represented by the refined semantic net, can be executed by search engines. This paper describes the methodology and its implementation in a prototype. An empirical evaluation shows that queries suggested by the prototype produce more relevant results than those obtained by the original queries. The research, thus, provides a successful demonstration of the use of existing knowledge sources to enhance the semantic content of Web queries. The paper concludes by identifying potential uses of such enhancements of search technology in organizational contexts.

Developing Mobile Commerce Applications

In this article, we deal with context-aware query processing in ad-hoc peer-to-peer networks. Each peer in such an environment has a database over which users execute queries. This database involves (a) relations which are locally stored and (b) virtual relations, all the tuples of which are collected from peers that are present in the network at the time when a query is posed. The objective of our work is to perform query processing in such an environment and, to this end, we start with a formal definition of the system model. Next, we formally define SQLP, an extension of SQL that covers the termination of queries, the failure of individual peers and the semantic characteristics of the peers of such a network. Moreover, we present a query execution algorithm as well as the formal definition of all the operators that take place in a query execution plan.

A Semantic Web-based Approach for Context-Aware User Query formulation and Information Retrieval

Formulating unambiguous queries in the Semantic Web applications is a challenging task for users. This article presents a new approach in guiding users to formulate clear requests based on their common nature of querying for information. The approach known as the front-end approach gives users an overview about the system data through a virtual data component which stores the extracted metadata of the data storage sources in the form of an ontology. This approach reduces the ambiguities in users’ requests at a very early stage and allows the query process to effectively perform in fulfilling users’ demands in a context-aware manner. Furthermore, the approach provides a powerful query engine, called context-aware querying, that recommends the appropriate query patterns according to the user’s querying context.