Exploiting Data-pattern-aware Vertical Partitioning to Achieve Fast and Low-cost Cloud Log Storage

A wireless body area network is a wireless network of wearable computing devices. BAN devices may be deployed inside or surface-mounted on the human body. WBAN tackles throughput, high query latency and energy consumption. In this paper, we proposed clique-based WBAN scheduling (CBWS) algorithm and cloud-based WBAN algorithm. First, the sensors are not active together at the same time it can be clustered into different groups to avoid interference. So colouring based technique is used to schedule all groups in a time slot. And these sensor data are provided to the database for user query processing. Then, a cloud-based technique is utilised to secure the stored information and optimise the real-time query processing is done to obtain energy minimisation and query latency. Also multi queue scheduling (MQS) algorithm is used. The experimental results minimal energy consumption, query latency and better throughput also provide secure powerful storage infrastructure in real-time environment.

Many of the data sources used in stream query processing are known to exhibit bursty behavior. We focus here on passive network monitoring, an application in which the data rates typically exhibit a large peak-to-average ratio. Provisioning a stream query processor to handle peak rates in such a setting can be prohibitively expensive. In this paper, we propose to solve this problem by provisioning the query processor for typical data rates instead of much higher peak data rates. To enable this strategy, we present mechanisms and policies for managing the tradeoffs between the latency and accuracy of query results when bursts exceed the steady-state capacity of the query processor. We describe the current status of our implementation and present experimental results on a testbed network monitoring application to demonstrate the utility of our approach

Prior research in human–computer interaction suggests that system‐level clarifications are necessary for understanding user intent and communicating effectively with the user. Such clarifications or explanations could contain the system's abstract knowledge of the search or a functional description of the search process (queries and information sources employed). While these interactions may aid the user and the agent in better understanding each other, very few studies have explored the influence of such clarifications on the users' search experience. This research examines whether and how system‐level clarifications (or explanations) affect the user experience when searching through spoken dialogues. We analyzed user satisfaction and preferences in systems with and without explicit clarifications in a within‐subjects Wizard‐of‐Oz user study. We recruited 25 participants and collected user–system interaction data for 50 search sessions. The user feedback was collected using pre‐ and post‐task surveys and exit interviews. Statistical and qualitative analysis of user responses yielded some interesting findings. While Wilcoxon Signed Rank Test found that using explicit system‐level clarifications had no positive influence on the user's search experience, the overall search experience degraded with system clarifications (Z = −2.066, p = 0.04). The user interview data provided valuable insights into how and when clarifications should be offered to the user.

The data intensive analytical workload becomes heavy burden for OLAP engine with increasing data volume, user population and query complexity. Large capacity random access memory, multi-level cache and multi-core hardware are main streams of computer. We propose a hardware-aware OLAP model named MOSS-DB which optimizes storage model according to data access features of dimensional tables and fact tables. A hard disk & main memory two-level storage model is employed to support directly dimensional tuple accessing join operator(DDTA-JOIN), DDTA-JOIN simplifies OLAP query processing by replacing traditional join operation with directly accessing dimensional tuple with memory address. So the star schema can be seen as virtual de-normalized table, OLAP query is also simplified to table scan, select and project operations. Query processing on sequence data structure is more suitable for multi-core parallel processing. Our proposal allows massive data DRDB(Disk Resident Database) storage technique to cooperate with MMDB(Main-Memory Database) processing technique, which breaks the main memory capacity limitation. The DDTA-JOIN operation can save cost for index, hash table, etc. For multi-core era, MOSS-DB can flexibly use parallel processing capability of CPU by dynamically dividing fact table into multiple scan partitions and gain maximum cache profit for shared dimensional data. In experiments, we measure that MOSS-DB outperforms conventional DRDB system, and it also outperforms MMDB in SSB testing.

An app store (i.e., Google Play) is a platform for mobile apps for almost every software and service. App stores allow users to browse and download apps and facilitate developers to keep an eye on their apps by providing ratings and reviews of the apps. App reviews may include the user's experience, information about bugs, request for new features, or rating of the app in word. The manual categorization of app reviews is critical and time-consuming for developers. Automatic classification of app reviews may help developers especially for fixing bugs on time. In this perspective, several approaches have been proposed for the automatic classification of reviews. However, none of them exploits the non-textual information of app reviews. In this paper, we propose a deep learning based approach for the classification of app reviews. It does not only leverage non-textual information of app reviews but also exploits a deep learning technique that has proved more accurate for the text classification in various domains. The approach first extracts textual and non-textual information of each app review, preprocesses the textual information, computes the sentiment of app reviews using Senti4SD, and determines the history of the reviewer includes the total number of reviews posted by the reviewer, and his submission rate (i.e., what percentages of his review have been submitted for the associated app). Second, we create a digital vector against each app review. Finally, we train a deep learning based multi-class classifier to classify app reviews. The proposed approach is evaluated on a public dataset, and the results suggest that it significantly improves the state of the art. It improves average precision from 75.72% to 95.49%, average recall from 69.40% to 93.94%, and f-measure from 72.41% to 94.71%, respectively.

Due to the loss of 3D information, accurate and robust 2D image feature matching remains challenging for many computer vision applications. This paper introduces a 2.5D feature that uses the disparity value from the light field Fourier disparity layer (FDL) as a rough proxy of scene depth. Without explicit depth estimation, a parameterized depth-degraded projection is proposed to construct the geometric transformation of paired features between two light fields. Then, we propose a parameterized learning solution to calculate the depth-degraded projection. This solution estimates a global constant fundamental matrix, a variable disparity-guided translation vector, and a depth compensation term using a very simple network. Although the 0.5D relative disparity provided by the FDL does not represent precise depth, it can also significantly reduce the depth ambiguity in feature matching. Therefore, the proposed solution achieves accurate feature-matching results by minimizing the sum of reprojection errors across all matching candidates. On the public light field feature-matching dataset, the proposed solution outperforms existing 2D image feature-matching solutions and light field feature-matching algorithms in terms of matching accuracy and robustness. The code is available online.

This paper addresses the resource allocation (RA) problem for orthogonal frequency division multiple access and single carrier frequency division multiple access based networks using hybrid automatic repeat request (HARQ) when considering energy efficiency (EE) as the metric to optimize. The RA is performed considering the Rayleigh channel, and assuming that only statistical channel state information is available, which fits well device-to-device (D2D) communications setup. Moreover, the use of practical modulation and coding schemes is taken into account. We formulate several RA problems with different EE criteria, subject to constraints on the minimum goodput and the maximum transmit power per-link. Based on a packet error probability approximation, we provide a framework which allows us to derive algorithms maximizing most of the common EE criteria, namely the sum of the EE of the links, the product of the EE, the EE of the link with the worst EE, and the EE of the whole network. The complexity of the proposed algorithms is also provided. The performance related to each criterion are evaluated through simulations and ranked according to the network configurations. We also illustrate on a practical example the real effectiveness of the EE criterion to achieve a better user experience than other common criteria such as the maximum sum rate and the minimum transmit power.

Background Simulated Consultation Assessment (SCA) preparation is critical for GP trainees, with the traditional approach relying on trained medical actors. However, limited actor availability, scheduling constraints and high costs often restrict practice opportunities. This evaluation aimed to assess a conversational artificial intelligent (AI) system as an educational tool for GP consultation skills training, examining its acceptability, perceived educational impact and implementation considerations for training. Methods We conducted an educational innovation evaluation with GP trainees and educators (n = 22) following implementation of a conversational AI simulation system for SCA preparation. The evaluation employed a mixed-methods design incorporating validated educational assessment measures across four domains: clinical authenticity, educational utility, user experience and technical performance. Participants’ perspectives were captured through structured questionnaires and open-ended feedback. Implementation considerations were assessed through cost-comparison analysis with traditional training methods. Results This educational innovation demonstrated strong acceptability among users, with particularly positive evaluations for clinical content authenticity (median 4.5, IQR 4–5) and educational value (median 4.5, IQR 4–5). Qualitative feedback revealed that the innovation successfully addressed key training needs, particularly around accessibility and practice frequency. More experienced educators (11+ years) rated the innovation significantly higher for educational utility (p < 0.05), suggesting potential for curriculum integration. The implementation analysis revealed resource advantages, with 24–84% cost reductions compared to traditional methods. Conclusion This conversational AI innovation offers GP educators a practical, cost-effective solution to training challenges, potentially addressing the modality mismatch between face-to-face training and video-based examinations. Implementation should focus on integration as a complementary curriculum tool, with ongoing evaluation of educational outcomes.

Scientific databases and data warehouses store large amounts of data ith several tables and attributes. For instance, the Sloan Digital Sky Survey (SDSS) astronomical database contains a large number of tables with hundreds of attributes, which can be queried in various combinations (Papadomanolakis &amp; Ailamaki, 2004). These queries involve many tables using binary operations, such as joins. To speed up these queries, many optimization structures were proposed that can be divided into two main categories: redundant structures like materialized views, advanced indexing schemes (bitmap, bitmap join indexes, etc.) (Sanjay, Chaudhuri &amp; Narasayya, 2000) and vertical partitioning (Sanjay, Narasayya &amp; Yang 2004) and non redundant structures like horizontal partitioning (Sanjay, Narasayya &amp; Yang 2004; Bellatreche, Boukhalfa &amp; Mohania, 2007) and parallel processing (Datta, Moon, &amp; Thomas, 2000; Stöhr, Märtens &amp; Rahm, 2000). These optimization techniques are used either in a sequential manner ou combined. These combinations are done intra-structures: materialized views and indexes for redundant and partitioning and data parallel processing for no redundant. Materialized views and indexes compete for the same resource representing storage, and incur maintenance overhead in the presence of updates (Sanjay, Chaudhuri &amp; Narasayya, 2000). None work addresses the problem of selecting combined optimization structures. In this paper, we propose two approaches; one for combining a non redundant structures horizontal partitioning and a redundant structure bitmap indexes in order to reduce the query processing and reduce the maintenance overhead, and another to exploit algorithms for vertical partitioning to generate bitmap join indexes. To facilitate the understanding of our approaches, for review these techniques in details.

Microblogging services have become among the most popular services on the web in the last few years. This led to significant increase in data size, speed, and applications. This paper presents Venus ; a system that supports real-time spatial queries on microblogs. Venus supports its queries on a spatial boundary $R$ and a temporal boundary $T$ , from which only the top- $k$ microblogs are returned in the query answer based on a spatio-temporal ranking function. Supporting such queries requires Venus to digest hundreds of millions of real-time microblogs in main-memory with high rates, yet, it provides low query responses and efficient memory utilization. To this end, Venus employs: (1) an efficient in-memory spatio-temporal index that digests high rates of incoming microblogs in real time, (2) a scalable query processor that prune the search space, $R$ and $T$ , effectively to provide low query latency on millions of items in real time, and (3) a group of memory optimization techniques that provide system administrators with different options to save significant memory resources while keeping the query accuracy almost perfect. Venus memory optimization techniques make use of the local arrival rates of microblogs to smartly shed microblogs that are old enough not to contribute to any query answer. In addition, Venus can adaptively, in real time, adjust its load shedding based on both the spatial distribution and the parameters of incoming query loads. All Venus components can accommodate different spatial and temporal ranking functions that are able to capture the importance of each dimension differently depending on the applications requirements. Extensive experimental results based on real Twitter data and actual locations of Bing search queries show that Venus supports high arrival rates of up to 64 K microblogs/second and average query latency of 4 msec.

Approximate query processing (AQP) technique speeds up query execution by reducing the amount of data that needs to be processed, while sacrificing the accuracy of the query result to some extent. AQP is essentially a trade-off between the accuracy of the query result and the query latency. However, the heuristic AQP optimization and error control mechanism used by the existing AQP system fails to meet the accuracy requirements of users. This paper proposes a deep learning-based error prediction model to guide AQP query optimization. By using this model, we can estimate the errors of candidate query plans and select the appropriate plans that can meet the accuracy requirement with high probability. Extensive experiments show that the AQP system proposed in this paper can outperform the state-of-the-art online sampling-based AQP approach.

The exponential growth of heterogeneous enterprise data has exposed the limitations of traditional Big Data architectures, which prioritize storage scalability over semantic understanding. This paper presents a Semantic Data Lake (SDL) framework that integrates Big Data technologies with knowledge graphs and ontology-driven reasoning to enhance enterprise decision support. The SDL architecture introduces a multi-layered system encompassing data ingestion, semantic annotation, ontology alignment, and graph-based reasoning for contextual query processing. Implemented using Hadoop, Spark, and GraphDB, the framework demonstrates superior performance in query efficiency, scalability, and semantic accuracy compared to conventional data lakes. Experimental evaluations show up to a 40% reduction in query latency and a 19% improvement in semantic precision, achieved through ontology mapping and reasoning-based query optimization. The results validate that semantic enrichment transforms data lakes into intelligent ecosystems capable of delivering explainable, context-aware analytics. The paper concludes by outlining future research directions, including AI-driven ontology learning, federated semantic integration, and hybrid reasoning for real-time knowledge discovery.

The lack of understanding of user experience with self-service query tools is a barrier to designing effective query tools and is what propelled this study. User actions were documented and transformed into networks of actions for qualitative analysis. Proficient use of self-service query tools requires significant technical experience. To decrease the user learning curve, additional user education is necessary for novice users.

Data encryption is necessary to keep user information secure and private on the cloud. However, adversaries can still learn valuable information about the encrypted data by observing data access patterns. To solve this issue, Oblivious RAMs (ORAMs) are proposed to hide access patterns. However, ORAMs are expensive and not suitable for deployment in a large database. In this work, we propose a range query algorithm while providing data access pattern protection based on uniform access frequency. In the preprocessing, multiple key-value pairs in the database are grouped and stored in each storage module, and we make copies for frequently accessed key-value pairs and also add some dummy key-value pairs on each storage module. In the online query processing, according to the range query length of the received query access request, we visit the specific storage module for the query and obtain the query result. Based on the techniques above, our method makes the uniform distribution of access frequency of data blocks in the database and achieves a security guarantee as strong as the state-of-the-art method. Compared with data queries that do not provide data access pattern protection, the ratio of network communication overhead is constant rather than logarithmic in ORAMs.

Large search engines process thousands of queries per second over billions of documents, making a huge performance gap between disjunctive and conjunctive queries. An important class of optimization techniques called top-k processing is therefore used to narrow the gap. In this paper, we propose an aggressive algorithm based on the document-at-a-time (DAAT) MaxScore, aiming at further reducing the query latency of disjunctive queries. Essentially, our approach, named Aggressive MaxScore (AMaxScore), can speed up quickly by fine-tuning the initial top-k threshold, which allows a first aggressive process and then a supplementary process if not enough results are returned. Experiments with TREC GOV2 collection show that our approach reduces disjunctive query processing time by almost 15.4% on average over the state-of-the-art MaxScore baseline, while still returns the same results as the disjunctive evaluation.KeywordsQuery processingTop-k processingDAATAggressive MaxScore