Stream Database Research Articles

A distributed B+Tree indexing method for processing range queries over streaming data

A data stream exhibits as a massive unbounded sequence of data elements continuously generated at a high rate. Stream databases raise new challenges for query processing due to both the streaming nature of data which constantly changes over time and the wider range of queries submitted by the user when compared with the traditional databases. In this paper, we propose a system architecture which includes components for both distributed indexing of streaming data and distributed processing of range queries on streaming data. Instead of creating a large and centralized B+Tree index structure, we create a set of small B+Tree indexes in such a way that a B+Tree index can be created for every partition of streaming data. We also design a distributed range search algorithm which can be used by each individual machine inside a Spark cluster to independently process range queries on each partition of streaming data. By exploiting the proposed system architecture, the process of indexing of streaming data and the process of querying over streaming data can be performed in a distributed and parallel manner. By performing several experiments, we demonstrate that our proposed indexing method is scalable and efficient for processing range queries on streaming data compared to the existing centralized B+Tree indexing methods and therefore, it can be used for applications involving data streams with a large volume of data elements and a large number of range queries.

Change Propagation Without Joins

We revisit the classical change propagation framework for query evaluation under updates. The standard framework takes a query plan and materializes the intermediate views, which incurs high polynomial costs in both space and time, with the join operator being the culprit. In this paper, we propose a new change propagation framework without joins, thus naturally avoiding this polynomial blowup. Meanwhile, we show that the new framework still supports constant-delay enumeration of both the deltas and the full query results, the same as in the standard framework. Furthermore, we provide a quantitative analysis of its update cost, which not only recovers many recent theoretical results on the problem, but also yields an effective approach to optimizing the query plan. The new framework is also easy to be integrated into an existing streaming database system. Experimental results show that our system prototype, implemented using Flink DataStream API, significantly outperforms other systems in terms of space, time, and latency.

Streaming Database System for Deaf People

Most of the traditional SQL keeps database records that do not often alternate. Models are client connection control systems or web and website online systems that replace like clockwork/seconds. Recently, programmers had composed dense codes to make database alter with converting or streaming records. Creating vast micro services to cope with stream records is exorbitant and often instable. SQL streaming database cope with record streams with insignificant coding and convey non-stop research for an extensive variety of forms of cutting-edge packages. A streaming database works with dynamic records that modification constantly. This converting record triggers other inquiries and packages as opposed to traditional way. The main characteristics of streaming database are continuous record of new data input, update the database status in real time, and answer online queries with consistency of database state. Standard database requires human to supply a reaction for streaming events. For example, when a customer buys an object on a commercial web enterprise human contribution triggers the reaction. The records in a streaming database are sort of streaming events in which it examines, gathers, and cycles those records streams and catches the records to be used gradually alarms, perceptions, questions, or packages. This research follows streaming database of deaf people to simultaneously provide understanding of events. Experiments showed that the streaming database structures are more appropriate for deaf people communicating with each other and with normal people as well. Extracted from the fact that 70% per cent of the sample were satisfied with the suggested system , more than 80% percentage of normal people are satisfying with the system, while more than 85% percentage of deaf and normal people are happy with the system. This type of on line updating and its applications can be applied for people with learning special needs in various ways.

Efficiency and Safety of Erector Spinae Plane Block in Percutaneous Nephrolithotomy: A Meta-Analysis Based on Randomized Controlled Trials

To pool the data of published studies using the meta-analysis method to provide high-level evidence for the use of ESPB in pain control after PCNL. Two main stream databases, Pubmed and Embase, were used to identify potentially included studies. Primary outcomes included pain visual analogue scale (VAS) at different time points, intraoperative fentanyl use, time to first rescue analgesia and total tramadol or paracetamol consumption. Secondary outcome was defined as side effects of nausea and vomiting. Fourteen studies were identified after database searching and 6 studies were included in the quantitative analysis. Overall, ESPB could significantly reduce the postoperative short-time (1-2 hours) and long-time (24 hours) VAS values (MD: -1.35, 95%CI: -1.71, -1.00 for short-time and MD: -0.39, 95%CI: -0.61, -0.17 for long-time) without significant heterogeneity. When it came to other primary outcomes, ESPB still showed its advantages in less intraoperative fentanyl use and total tramadol/paracetamol consumption, and longer time to first rescue analgesia. Complications were similar between groups (OR:0.90, 95%CI:0.38-2.14). ESPB was an efficient and safe procedure for postoperative pain management in PCNL. More RCTs with larger sample size are still needed.

Database Click Stream of E-commerce Functional

The availability of e-commerce functionality that suits for user needs in e-commerce applications will increase the sustainability of application usage and can provide benefits for its users. Many e-commerce applications have been developed, but based on the results of previous research, these e-commerce applications do not pay attention to the availability of functionality and its advantages in the application. A database design to store functional clickstream ecommerce is required to determine the number of features that users are accessing. Database application development is the activity of identifying real-world requirements, analyzing requirements, designing system data and functions, and then implementing operations in the system. The database life cycle method is used to build a database in this study. This research has produced a click stream database that has added functional attributes available in e-commerce, which are accessed by users. The results also show the addition of several tables that will facilitate the management of click stream data functionality from e-commerce applications.

Corporate Governance and Corporate Reputation: The Role of Environmental and Social Reporting Quality

Corporate reputation is companies’ most valuable asset as it can position them to gain competitive advantages that lead to sustainable performance. Therefore, understanding the factors that influence corporate reputation is vital for a company’s survival. The study objectives were to investigate the effects of corporate governance and the quality of environmental and social reporting on corporate reputation. Additionally, this study examined the role of environmental and social reporting quality on the relationship between these two variables. This study used secondary data collected from multiple sources such as the Thomson Data Stream database and annual reports of publicly listed Malaysian companies between 2017 and 2018. The results showed that corporate governance effectiveness and environmental and social reporting quality positively influence corporate reputation. Additionally, the quality of environmental and social reporting mediates the relationship between corporate governance and corporate reputation. This study bridges research gaps by providing evidence for the impact of effective corporate governance, specifically board diversity, on corporate reputation in Malaysia. The findings can help companies to establish criteria and qualifications for the appointment of new board members. The members must have the right combination of skills, knowledge, experience and independent elements that enable them to make decisions to meet companies’ objectives.

English

The main aim of this study was to examine the influence of gender diversity on financial performance of EAC’s listed companies and to compare influence of gender diversity on company’s financial performance before and after the operationalisation of the East African Community (EAC) Common Market in 2010. The authors adopted a positivist paradigm in a quantitative analysis using non-probability sampling to select forty-two EAC listed companies. They developed hypothesises basing on secondary data from data stream database and annual reports. SPSS was used to generate correlation, and regression results. The findings indicated that gender diversity of the board has no statistically significant influence on company financial performance measured by Return on Assets (ROA), Return on Equity (ROE), Tobin Q ratio (TBQ) and Price Earnings Ratio (PER). Secondly, the authors discovered no changes in gender diversity for most listed companies for the period before and after operationalisation of the EAC - Common Market.   Key words: Gender, diversity.

RHUPS

Databases that deal with the real world have various characteristics. New data is continuously inserted over time without limiting the length of the database, and a variety of information about the items constituting the database is contained. Recently generated data has a greater influence than the previously generated data. These are called the time-sensitive non-binary stream databases, and they include databases such as web-server click data, market sales data, data from sensor networks, and network traffic measurement. Many high utility pattern mining and stream pattern mining methods have been proposed so far. However, they have a limitation that they are not suitable to analyze these databases, because they find valid patterns by analyzing a database with only some of the features described above. Therefore, knowledge-based software about how to find meaningful information efficiently by analyzing databases with these characteristics is required. In this article, we propose an intelligent information system that calculates the influence of the insertion time of each batch in a large-scale stream database by applying the sliding window model and mines recent high utility patterns without generating candidate patterns. In addition, a novel list-based data structure is suggested for a fast and efficient management of the time-sensitive stream databases. Moreover, our technique is compared with state-of-the-art algorithms through various experiments using real datasets and synthetic datasets. The experimental results show that our approach outperforms the previously proposed methods in terms of runtime, memory usage, and scalability.

SUSTAINABILITY REPORT, OWNERSHIP TYPES, AND COST OF CAPITAL: EVIDENCE ON INVESTORS’ REACTION OF INDONESIAN LISTED COMPANIES

Purpose: This research aims to examine investors’ reactions to the Sustainability Report (SR) and the moderating effect of ownership types associated with stakeholders’ interdependence theory.
 Methodology: This research uses purposive sampling method to choose the samples. The population of the samples is Indonesian listed companies that disclose sustainability reports from 2014 to 2016. This research uses secondary data using several data sources, such as the annual report, financial report, sustainability report, data stream database, and another secondary source.
 Results: In general, empirical evidence indicate that investors appreciate sound SR performance by the tendency to give a discount on their investment; hence, they charge low Cost of Capital. Also, types of ownership do have a moderating effect on investors’ reaction, suggesting the existence of interdependent among stakeholders group.
 Implications: This research captures investors' reactions by looking at the Cost of the Capital bear by the company as a proxy for investor reaction on the perceived risk associated with the sustainability issues. Moreover, this research converts the Cost of Capital as a continuous variable into a categorical variable of high and low.

Streaming video database features: What do faculty and students really want?

The adoption of streaming video by academic libraries is widespread, yet little is known about the experiences of faculty and students who use streaming video. This article describes a usability study of three popular streaming video databases. Faculty and student participants completed a series of tasks in each database and answered questions about their use of streaming video. Faculty liked complete and accurate metadata and text descriptions, the use of thumbnail images, accessibility, ease of use, the availability of film segments, the ability to easily share content, and the availability of time-stamped, hyperlinked transcripts. Students liked easy-to-use, intuitive interfaces.

Towards a Semantically Enriched Local Dynamic Map

With the increasing availability of Cooperative Intelligent Transport Systems, the Local Dynamic Map (LDM) is becoming a key technology for integrating static, temporary, and dynamic information in a geographical context. However, existing ideas do not leverage the full potential of the LDM approach, as an LDM contains streaming data and varying implicit information which are not captured by current models. We aim to provide a semantically enriched LDM that applies Semantic Web technologies, in particular ontologies, in combination with spatial stream databases. This allows us to define an enhanced world model, to derive model properties, to infer new information, and to offer expressive query capabilities over streams. We introduce our envisioned architecture which includes an LDM ontology, an integration and annotation framework, and a stream query answering component. We also sketch three application scenarios that illustrate the usability and benefits of our approach, thus we provide an in-depth validation of the scenarios in an experimental prototype.

Differed Restructuring of Neural Connectome Using Evolutionary Neurodynamic Algorithm for Improved M2M Online Learning

Ontological nodes and training material, in any case, contain however a little part of the quickly developing volume of unstructured contents accessible across training datasets ranging from the greater part of heterogeneous "learning". Recovery of these assets is constrained to keyword search, which is in a far-reaching way incognizant in regards to the normal sequential semantic connection in which the query is often requested and all the more essentially for more importantly for development of a perception about a learned concept through the sequential arrangement of information. Numerous core topics covered in several referential sources when no two are precisely similar, with one frequently more suitable when seen in a connection of an ontological relational modalities of semantic web and online training of network based on continuous streaming database; while other on giving major insights of practical implementation. The objective of this work is to present an evolutionary neurodynamic framework to mine such process based learning validated by the visual recording & extraction of neural weights through neurodynamic neural experiences when going through the referenced content. The motivation of this behavioral modeling of autonomous machine’s reaction working as the feedback of the learning content set forth above hinges on the principal capacity to gather a structure of a curricular graph automatically from the training datasets. As it empowers the acknowledgment of: 1) Sharing of neural experiences with the help of structurally sequenced networked topology of such large sample of training datasets resources - with the idea of dynamic analysis and linking of key resources in the mechanism with which the learner develops a general concept about the subject which can be latter re-used or shared with other user's and thus facilitating the qualitative evaluation of the ontological nodes content and its contribution in facilitating deep grasp of the data from feedback loop. 2) Obtaining information driven ontological knowledge base from the curricular relations inside and crosswise over other learning resources and 3) Generating customized, focused curricula, utilizing the tremendous assets of heterogeneous modalities on the training datasets.

Pyramid sketch

Sketch is a probabilistic data structure, and is used to store and query the frequency of any item in a given multiset. Due to its high memory efficiency, it has been applied to various fields in computer science, such as stream database, network traffic measurement, etc. The key metrics of sketches for data streams are accuracy, speed, and memory usage. Various sketches have been proposed, but they cannot achieve both high accuracy and high speed using limited memory, especially for skewed datasets. To address this issue, we propose a sketch framework, the Pyramid sketch, which can significantly improve accuracy as well as update and query speed. To verify the effectiveness and efficiency of our framework, we applied our framework to four typical sketches. Extensive experimental results show that the accuracy is improved up to 3.50 times, while the speed is improved up to 2.10 times. We have released our source codes at Github [1].

Climate, Demography, and Zoogeography Predict Introgression Thresholds in Salmonid Hybrid Zones in Rocky Mountain Streams.

Among the many threats posed by invasions of nonnative species is introgressive hybridization, which can lead to the genomic extinction of native taxa. This phenomenon is regarded as common and perhaps inevitable among native cutthroat trout and introduced rainbow trout in western North America, despite that these taxa naturally co-occur in some locations. We conducted a synthetic analysis of 13,315 genotyped fish from 558 sites by building logistic regression models using data from geospatial stream databases and from 12 published studies of hybridization to assess whether environmental covariates could explain levels of introgression between westslope cutthroat trout and rainbow trout in the U.S. northern Rocky Mountains. A consensus model performed well (AUC, 0.78–0.86; classification success, 72–82%; 10-fold cross validation, 70–82%) and predicted that rainbow trout introgression was significantly associated with warmer water temperatures, larger streams, proximity to warmer habitats and to recent sources of rainbow trout propagules, presence within the historical range of rainbow trout, and locations further east. Assuming that water temperatures will continue to rise in response to climate change and that levels of introgression outside the historical range of rainbow trout will equilibrate with those inside that range, we applied six scenarios across a 55,234-km stream network that forecast 9.5–74.7% declines in the amount of habitat occupied by westslope cutthroat trout populations of conservation value, but not the wholesale loss of such populations. We conclude that introgression between these taxa is predictably related to environmental conditions, many of which can be manipulated to foster largely genetically intact populations of westslope cutthroat trout and help managers prioritize conservation activities.

Learning concept-drifting data streams with random ensemble decision trees

Few online classification algorithms based on traditional inductive ensembling, such as online bagging or boosting, focus on handling concept drifting data streams while performing well on noisy data. Motivated by this, an incremental algorithm based on Ensemble Decision Trees for Concept-drifting data streams (EDTC) is proposed in this paper. Three variants of random feature selection are introduced to implement split-tests and two thresholds specified in Hoeffding Bounds inequality are utilized to distinguish concept drifts from noisy data. Extensive studies on synthetic and real streaming databases demonstrate that our algorithm of EDTC performs very well compared to several known online algorithms based on single models and ensemble models. A conclusion is hence drawn that multiple solutions are provided for learning from concept drifting data streams under noise.

랜드마크 윈도우 기반의 빈발 패턴 마이닝 기법의 분석 및 성능평가

ABSTRACT With the development of online service, recent forms of databas es have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment o f marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artific ial intelligence. Landmark window-based frequent pattern mining , one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, inste ad of all the data. In this paper, we analyze and evaluate the tec hniques of the well-known landmark window-based frequent patter n mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns a s soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reaso n, such algorithms are also called online mining approaches. We evaluat e and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms’ total runtime and average processing time per transaction. In addition, to compare the ef ficiency of storage structures between them, their maximum memo ry usage is also evaluated. Lastly, we show how stably the two alg orithms conduct their mining works with respect to the database s that feature gradually increasing items. With respect to the evaluat ion results of mining time and transaction processing, hMiner h as higher speed than that of Lossy counting. Since hMiner stores candidat e frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a l attice manner; thus, it has to search for multiple nodes in ord er to access the candidate frequent patterns. On the other hand, hMiner show s worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash’s bucket s, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner pres ents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is

Sharing across Multiple MapReduce Jobs

Large-scale data analysis lies in the core of modern enterprises and scientific research. With the emergence of cloud computing, the use of an analytical query processing infrastructure can be directly associated with monetary cost. MapReduce has been a popular framework in the context of cloud computing, designed to serve long-running queries (jobs) which can be processed in batch mode. Taking into account that different jobs often perform similar work, there are many opportunities for sharing. In principle, sharing similar work reduces the overall amount of work, which can lead to reducing monetary charges for utilizing the processing infrastructure. In this article we present a sharing framework tailored to MapReduce, namely, <tt>MRShare</tt>. Our framework, <tt>MRShare</tt>, transforms a batch of queries into a new batch that will be executed more efficiently, by merging jobs into groups and evaluating each group as a single query. Based on our cost model for MapReduce, we define an optimization problem and we provide a solution that derives the optimal grouping of queries. Given the query grouping, we merge jobs appropriately and submit them to MapReduce for processing. A key property of <tt>MRShare</tt> is that it is independent of the MapReduce implementation. Experiments with our prototype, built on top of Hadoop, demonstrate the overall effectiveness of our approach. <tt>MRShare</tt> is primarily designed for handling I/O-intensive queries. However, with the development of high-level languages operating on top of MapReduce, user queries executed in this model become more complex and CPU intensive. Commonly, executed queries can be modeled as evaluating pipelines of CPU-expensive filters over the input stream. Examples of such filters include, but are not limited to, index probes, or certain types of joins. In this article we adapt some of the standard techniques for filter ordering used in relational and stream databases, propose their extensions, and implement them through <tt>MRAdaptiveFilter</tt>, an extension of <tt>MRShare</tt> for expensive filter ordering tailored to MapReduce, which allows one to handle both single- and batch-query execution modes. We present an experimental evaluation that demonstrates additional benefits of <tt>MRAdaptiveFilter</tt>, when executing CPU-intensive queries in <tt>MRShare</tt>.

Optimization Issues of Querying and Evolving Sensor and Stream Databases Volume 38, Issue 8, November 2013, Pages 1184–1186

Optimization Issues of Querying and Evolving Sensor and Stream Databases Volume 38, Issue 8, November 2013, Pages 1184–1186

Mining maximal frequent patterns by considering weight conditions over data streams

Frequent pattern mining over data streams is currently one of the most interesting fields in data mining. Current databases have needed more immediate processes since enormous amounts of data are being accumulated and updated in real time. However, existing traditional approaches have not been entirely suitable for a data stream environment since they operate with more than two database scans. Moreover, frequent pattern mining over data streams mostly generates an enormous number of frequent patterns, thereby causing a significant amount of overheads. In addition, as weight conditions are very useful factors in reflecting importance for each object in the real world, it is necessary to apply them to the mining process in order to obtain more practical, meaningful patterns. To consider and solve these problems, we propose a novel method for mining Weighted Maximal Frequent Patterns (WMFPs) over data streams, called MWS (Maximal frequent pattern mining with Weight conditions over data Streams). MWS guarantees efficient mining performance in the data stream environment by scanning stream databases only once, and prevents overheads of pattern extractions with an abbreviated notation: a maximal frequent pattern form instead of the general one. Furthermore, MWS contributes to enhanced reliability of the mining results by applying weight conditions to each element of the data streams. Extensive experiments report that MWS has outstanding performance in comparison to previous algorithms.

Non-smoking non-drinking elderly females: a clinically distinct subgroup of oral squamous cell carcinoma patients

There is growing interest in non-smoking non-drinking (NSND) patients presenting with oral squamous cell carcinoma (OSCC). There are, however, few published reports of OSCC in the elderly. We describe a subgroup of elderly NSND patients presenting with OSCC. Patients with SCC of the oral cavity were retrospectively assessed from the Head and Neck Oncology Tumour Stream database of the Royal Melbourne Hospital. Epidemiological and clinical data for 169 consecutive patients were reviewed and analysed. NSND patients were more likely to be females with a higher median age at presentation. They were more likely to have maxillary alveolus tumours and oral tongue tumours, with retromolar or mandibular tumours less likely. Second primary tumours for this subgroup were confined to the oral cavity. NSND elderly females experienced a worse disease-specific mortality. We have identified a distinct subgroup of elderly female patients presenting with OSCC not associated with the traditional risk factors of tobacco and alcohol, who have a worse prognosis. Altered management algorithms may prove beneficial for these patients, and further investigation and genetic analysis are required to delineate the aetiology of these carcinomas.