Deep active reinforcement learning for privacy preserve data mining in 5G environments

Finding frequent patterns identifies the most important patterns in data sets. Due to the huge and high-dimensional nature of transactional data, classical pattern mining techniques suffer from the limitations of dimensions and data annotations. Recently, data mining while preserving privacy is considered an important research area in recent decades. Information privacy is a tradeoff that must be considered when using data. Through many years, privacy-preserving data mining (PPDM) made use of methods that are mostly based on heuristics. The operation of deletion was used to hide the sensitive information in PPDM. In this study, we used deep active learning to hide sensitive operations and protect private information. This paper combines entropy-based active learning with an attention-based approach to effectively detect sensitive patterns. The constructed models are then validated using high-dimensional transactional data with attention-based and active learning methods in a reinforcement environment. The results show that the proposed model can support and improve the decision boundaries by increasing the number of training instances through the use of a pooling technique and an entropy uncertainty measure. The proposed paradigm can achieve cleanup by hiding sensitive items and avoiding non-sensitive items. The model outperforms greedy, genetic, and particle swarm optimization approaches.

Estimating the true arrival, balking, and reneging processes from censored transactional data: a simulation-based approach

The transactional data typically collected/available on queueing systems are often subject to censoring as unsuccessful arrivals due to balking and/or unserved entities due to reneging are not recorded. In fact, in many situations, the true arrival, balking, and reneging events are unobservable, making it virtually impossible to collect data on these stochastic processes—information that is crucial for capacity planning and process improvement decisions. The objective of this paper is to estimate the true (latent) external arrival, balking, and reneging processes in queueing systems from such censored transactional data. The estimation problem is formulated as an optimization model and an iterative simulation-based inference approach is proposed to find appropriate input models for these stochastic processes. The proposed method is applicable in any complex queueing situation as long as it can be simulated. The problem is investigated under both known and unknown reneging distribution. Through extensive simulation experiments, general guidelines are provided for specifying the parameters of the proposed approach, namely, sample size and number of replications. The proposed approach is also validated through a real-world application in a call center, where it successfully estimates the underlying arrival, balking, and reneging distributions. Finally, to enable reproducibility and technology transfer, a working example, including all codes and sample data, are made available in an open online data repository associated with this paper.

A Conversation with Margo Seltzer and Mike Olson

Kirk McKusick sat down with Margo Seltzer and Mike Olson to discuss the history of Berkeley DB, for which they won the ACM Software System Award in 2021. Kirk McKusick has spent his career as a BSD and FreeBSD developer. Margo Seltzer has spent her career as a professor of computer science and as an entrepreneur of database software companies. Mike Olson started his career as a software developer and later started and managed several open-source software companies. Berkeley DB is a production-quality, scalable, NoSQL, Open Source platform for embedded transactional data management.

HTAP With Reactive Streaming ETL

In database management systems (DBMSs), query workloads can be classified as online transactional processing (OLTP) or online analytical processing (OLAP). These often run within separate DBMSs. In hybrid transactional and analytical processing (HTAP), both workloads may execute within the same DBMS. This article shows that it is possible to run separate OLTP and OLAP DBMSs, and still support timely business decisions from analytical queries running off fresh transactional data. Several setups to manage OLTP and OLAP workloads are analysed. Then, benchmarks on two industry standard DBMSs empirically show that, under an OLTP workload, a row-store DBMS sustains a 1000 times higher throughput than a columnar DBMS, whilst OLAP queries are more than 4 times faster on a columnar DBMS. Finally, a reactive streaming ETL pipeline is implemented which connects these two DBMSs. Separate benchmarks show that OLTP events can be streamed to an OLAP database within a few seconds.

HTAP With Reactive Streaming ETL

In database management systems (DBMSs), query workloads can be classified as online transactional processing (OLTP) or online analytical processing (OLAP). These often run within separate DBMSs. In hybrid transactional and analytical processing (HTAP), both workloads may execute within the same DBMS. This article shows that it is possible to run separate OLTP and OLAP DBMSs, and still support timely business decisions from analytical queries running off fresh transactional data. Several setups to manage OLTP and OLAP workloads are analysed. Then, benchmarks on two industry standard DBMSs empirically show that, under an OLTP workload, a row-store DBMS sustains a 1000 times higher throughput than a columnar DBMS, whilst OLAP queries are more than 4 times faster on a columnar DBMS. Finally, a reactive streaming ETL pipeline is implemented which connects these two DBMSs. Separate benchmarks show that OLTP events can be streamed to an OLAP database within a few seconds.

Crisis Mitigation through Cash Assistance to Increase Local Consumption Levels—A Case Study of a Bimonetary System in Barcelona, Spain

Subsidies in the form of direct transfers from the government to citizens constitute a powerful mechanism for crisis mitigation and for the alleviation of economic inequalities. However, the connection between direct transfers of cash assistance to selected individual beneficiaries and the prosperity of their immediate surrounding local economy has not been sufficiently explored. This paper presents a case study which analyzes the effects of allocating cash assistance in the form of a local currency. It shows that, under certain conditions, such a transfer not only provides the beneficiaries with additional purchasing power to satisfy their needs but also that the monetary injection benefits local SMEs by generating additional turnover. Using transactional data from the system, some indicators are proposed to analyze the properties of the system, namely, user satisfaction, total and average income generated by local businesses, the local multiplier, the recirculation of the local currency, and the velocity of its circulation. Our findings indicate that cash assistance provided in the REC local currency could contribute to local economic development and financial stability by sustaining local commerce, while preserving most of the original positive effects of cash assistance in a legal tender.

A chaos theory inspired, asynchronous two-way encryption mechanism for cloud computing

Data exchange over the Internet and other access channels is on the rise, leads to the insecurity of consequences. Many experiments have been conducted to investigate time-efficient and high-randomized encryption methods for the data. The latest studies, however, have still been debated because of different factors. The study outcomes do not yield completely random keys for encryption methods that are longer than this. Prominent repetition makes the processes predictable and susceptible to assaults. Furthermore, recently generated keys need recent algorithms to run at a high volume of transactional data successfully. In this article, the proposed solutions to these two critical issues are presented. In the beginning, one must use the chaotic series of events for generating keys is sufficient to obtain a high degree of randomness. Moreover, this work also proposes a novel and non-traditional validation test to determine the true randomness of the keys produced from a correlation algorithm. An approximate 100% probability of the vital phase over almost infinitely long-time intervals minimizes the algorithms’ complexity for the higher volume of data security. It is suggested that these algorithms are mainly intended for cloud-based transactions. Data volume is potentially higher and extremely changeable 3% to 4% of the improvement in data transmission time with suggested algorithms. This research has the potential to improve communication systems over ten years by unblocking decades-long bottlenecks.

Hadoop Based Generic Template for Performing Sentiment Analysis Using Apache PIG

Every day over 2.5 quintillion data is generated using various channels like online surveys, transactional data tracking, social media monitoring, etc. Out of these majority of the data is generated using social media platforms. This raw data contains information that can be used for industrial, economic, social and business purposes. To facilitate this, sentiment analysis has become a prospect for various tech-based industry giants to review and analyze their products. Hadoop has been established as one of the best tools for storing, processing, and streaming data in the market. In this paper, we present a generic approach to performing sentiment analysis using Apache PIG which classifies the given data taken from a dataset to either positive or negative to get the people’s sentiment over an object or an issue.