Efficient Aggregated Deliveries with Strong Guarantees in an Event-based Distributed System

A popular approach to designing large scale distributed systems is to follow an event-based approach. In an event-based approach, a set of software components interact by producing and consuming events. The event-based model allows for the decoupling of software components, allowing distributed systems to scale to a large number of components. Event correlation allows for higher order reasoning of events by constructing complex events from single, consumable events. In many cases, event correlation applications rely on centralized setups or broker overlay networks. In the case of centralized setups, the guarantees for complex event delivery are stronger, however, centralized setups create performance bottlenecks and single points of failure. With broker overlays, the performance and fault tolerance are improved but at the cost of weaker guarantees

REAKSI INVESTOR PASAR MODAL PADA PENGUMUMAN PENGHAPUSAN KODE BROKER

The policy of removing the broker codes of securities companies on the Indonesia Stock Exchange (IDX) has occurred for the first time in the Indonesian capital market. This policy was expected to prevent investors from buying shares by following transactional activities carried out by brokers, because using the broker code of a securities company, it is possible to know which securities company buys or sells a particular stock. The aim of this research is to  analyze the differences in trading volume, trading frequency, and abnormal returns before and  after the removal of broker codes so that the IDX can find out the effectiveness of the removal of the broker codes of securities companies and to find out whether investors have knowledge or not in making decisions to invest in stocks. The non-probabilistic purposive sampling technique was used and 738 companies listed on the IDX were used as the research sample. This study used an event study approach and data were analyzed using the Wilcoxon Signed Ranks Test. The results show that there were differences in trading volume, trading frequency, and abnormal returns before and after the removal of broker codes. Based on these findings, the removal of broker codes is an event that has information content and investors are expected to be able to conduct a deeper analysis when investing in the capital market. Keywords: trading volume; trading frequency; abnormal returns; broker code removal. Peristiwa kebijakan penghapusan kode broker perusahaan sekuritas pada Bursa Efek Indonesia (BEI) adalah pertama kali terjadi di pasar modal Indonesia. Kebijakan tersebut diharapkan dapat mencegah perilaku ikut-ikutan investor dalam membeli saham dari aktivitas transaksi yang dilakukan oleh broker karena melalui kode broker perusahaan sekuritas dapat diketahui broker dari perusahaan sekuritas mana yang banyak membeli atau menjual saham tertentu. Tujuan riset ini adalah untuk menganalisis perbedaan volume perdagangan, frekuensi perdagangan, dan return tidak normal (abnormal return) sebelum dan sesudah penghapusan kode broker, sehingga BEI dapat mengetahui seberapa efektif penghapusan kode broker perusahaan sekuritas tersebut dan untuk mengetahui investor memiliki pengetahuan atau tidak dalam pengambilan keputusan untuk berinvestasi saham selama ini. Teknik pengambilan sampel yang dipergunakan adalah non-probabilistic sampling dengan jenis purposive sampling, dengan 738 perusahaan yang tercatat di BEI yang akan dipergunakan sebagai sampel penelitian. Riset ini menggunakan pendekatan event study dan data dianalisis menggunakan Wilcoxon Signed Ranks Test. Hasil analisis data menunjukkan adanya perbedaan volume perdagangan, frekuensi perdagangan, dan abnormal return sebelum dan sesudah penghapusan kode broker. Berdasar pada hal tersebut penghapusan kode broker merupakan peristiwa yang memiliki kandungan informasi dan investor diharapkan dapat melakukan analisis lebih dalam pada saat hendak berinvestasi pada pasar modal. Kata kunci:  volume perdagangan; frekuensi perdagangan; abnormal return; penghapusan kode broker

The ANTARES Astronomical Time-Domain Event Broker

We describe the Arizona-NOIRLab Temporal Analysis and Response to Events System (ANTARES), a software instrument designed to process large-scale streams of astronomical time-domain alerts. With the advent of large-format CCDs on wide-field imaging telescopes, time-domain surveys now routinely discover tens of thousands of new events each night, more than can be evaluated by astronomers alone. The ANTARES event broker will process alerts, annotating them with catalog associations and filtering them to distinguish customizable subsets of events. We describe the data model of the system, the overall architecture, annotation, implementation of filters, system outputs, provenance tracking, system performance, and the user interface.Comment: 24 Pages, 8 figures, Accepted by A

How does the market react to your order flow?

We present an empirical study of the intertwined behaviour of members in a financial market. Exploiting a database where the broker that initiates an order book event can be identified, we decompose the correlation and response functions into contributions coming from different market participants and study how their behaviour is interconnected. We find evidence that (1) brokers are very heterogeneous in liquidity provision -- some are consistently liquidity providers while others are consistently liquidity takers. (2) The behaviour of brokers is strongly conditioned on the actions of {\it other} brokers. In contrast brokers are only weakly influenced by the impact of their own previous orders. (3) The total impact of market orders is the result of a subtle compensation between the same broker pushing the price in one direction and the liquidity provision of other brokers pushing it in the opposite direction. These results enforce the picture of market dynamics being the result of the competition between heterogeneous participants interacting to form a complicated market ecology.Comment: 22 pages, 5+9 figure

Processing count queries over event streams at multiple time granularities

Management and analysis of streaming data has become crucial with its applications in web, sensor data, network tra c data, and stock market. Data streams consist of mostly numeric data but what is more interesting is the events derived from the numerical data that need to be monitored. The events obtained from streaming data form event streams. Event streams have similar properties to data streams, i.e., they are seen only once in a fixed order as a continuous stream. Events appearing in the event stream have time stamps associated with them in a certain time granularity, such as second, minute, or hour. One type of frequently asked queries over event streams is count queries, i.e., the frequency of an event occurrence over time. Count queries can be answered over event streams easily, however, users may ask queries over di erent time granularities as well. For example, a broker may ask how many times a stock increased in the same time frame, where the time frames specified could be hour, day, or both. This is crucial especially in the case of event streams where only a window of an event stream is available at a certain time instead of the whole stream. In this paper, we propose a technique for predicting the frequencies of event occurrences in event streams at multiple time granularities. The proposed approximation method e ciently estimates the count of events with a high accuracy in an event stream at any time granularity by examining the distance distributions of event occurrences. The proposed method has been implemented and tested on di erent real data sets and the results obtained are presented to show its e ectiveness

Intelligent event broker: a complex event processing system in big data contexts

In Big Data contexts, many batch and streaming oriented technologies have emerged to deal with the high valuable sources of events, such as Internet of Things (IoT) platforms, the Web, several types of databases, among others. The huge amount of heterogeneous data being constantly generated by a world of interconnected things and the need for (semi)-automated decision-making processes through Complex Event Processing (CEP) and Machine Learning (ML) have raised the need for innovative architectures capable of processing events in a streamlined, scalable, analytical, and integrated way. This paper presents the Intelligent Event Broker, a CEP system built upon flexible and scalable Big Data techniques and technologies, highlighting its system architecture, software packages, and classes. A demonstration case in Bosch’s Industry 4.0 context is presented, detailing how the system can be used to manage and improve the quality of the manufacturing process, showing its usefulness for solving real-world event-oriented problems.This work has been supported by FCT –Fundação para a Ciência e Tecnologiawithin the Project Scope: UID/CEC/00319/2019 and the Doctoral scholarship PD/BDE/135101/2017. This paper uses icons made by Freepik, from www.flaticon.com

Can a Homeowner Benefit Agreement Run with the Land to Bind Successors?

Laws governing the enforceability of brokerage contracts are largely uniform and provide stable outcomes in the event of broker or client breach. Brokerage contracts reflect a hybrid of property and contract law principles that work to provide predictabl

Broker Protocol For Automated Event Scheduling By Virtual Assistant

When planning an event, people often need to coordinate and negotiate with other parties to determine a time slot that is suitable for all parties. Figuring out and confirming the most suitable arrangements can involve substantial communication overhead, thus making the process slow and tedious, especially when several parties are involved. Further, such an approach can be invasive because it can require visibility into the calendars of all parties involved. This disclosure describes techniques that enable automated communication and coordination among virtual assistants to schedule an appointment involving multiple parties. User-permitted information such as the user’s calendar, routines and preferences, etc. can be accessed by the virtual assistant to implement a broker protocol to determine a time for the appointment that works for the multiple parties