Bill of materials (BOMs) are at the heart of every manufacturing process. Especially large BOMs can be found in the automotive industry, where a complex and highly variable product meets high production volumes.
Drawing from the experiences made in an ongoing real world project for a major car manufacturer, Andreas will provide an in-depth view how Apache Solr and Apache Spark were used to power an innovative architecture that provides lightning-fast BOM explosions, demand forecasts and scenario-based planning on 20 billion records per scenario.
Religious Minorities under Rising Discriminations and Violence in IndonesiaAndreas Harsono
In 2006, the Indonesian government introduced a new regulation called "religious harmony" and established the so-called Religious Harmony Forum as advisory body in all of its regencies, cities and provinces. It advises regents, mayors, and governors on religious affairs. Its basic principle is that the majority religion should protect the minorities and the minorities should respect the majority, meaning Islam. It triggered the rise of discrimination and violence against religious minorities in Indonesia.
Per Anhalter durch den Cloud Native Stack (extended edition)QAware GmbH
OOP 2017, München: Vortrag von Mario-Leander Reimer (@LeanderReimer, Cheftechnologe bei QAware).
Abstract: Cloud-Größen wie Google und Twitter haben die Kern-Bausteine ihrer Infrastruktur quelloffen verfügbar gemacht. Jeder kann nun selbst cloud-native Anwendungen entwickeln – Anwendungen, die in der Cloud zuverlässig laufen und fast beliebig skalieren. Dafür muss man sich seinen Cloud Native Stack zusammenstellen aus Bausteinen wie Docker, DC/OS, Kubernetes und Spring Cloud. Wir bereisen die wichtigsten Technologien und Architekturmuster rund um den Cloud Native Stack und bringen dabei eine Beispielanwendung schrittweise in der Cloud zum Laufen.
Zielpublikum: Architekten, Entwickler, Projektleiter, Entscheider, Cloud Native Nerds
Voraussetzungen: Grundkenntnisse in Java
Schwierigkeitsgrad: Anfänger
Extended Abstract: Jeder spricht über die Cloud, jeder ist in der Cloud. Dennoch scheinen die Akzeptanz und der Wille, die damit verbundenen Technologien im eigenen Unternehmen gewinnbringend einzusetzen, nach wie vor eher zögerlich. Dieser Vortrag soll dazu beitragen, die bestehenden Vorbehalte abzubauen. Wir werden nicht viele Worte zur Theorie von Cloud-nativen Anwendungen verschwenden. Stattdessen werden wir zeigen, dass der Cloud Native Stack reif ist für unternehmenskritische Anwendungen.
Madhav Kulkarni (Dow Chemicals, India)
Classification Codes in a patent document are given by Examiner. Due to time constraint and background/interest of the Examiner, some classification codes which could have been applied to the patent document are not applied. Though patent searchers know the inadequacy in searching Classification Codes, at times it is a useful field for focussed searches. Analysis of retrieved patent documents by Codes, Codes by Year and Codes by Assignee is typical but may not really represent the dataset truly. Here the presenter makes a case of Classification Codes and proposes solutions that would improve reliance on search and analysis of Classification Codes.
Understanding the architecture of MariaDB ColumnStoreMariaDB plc
MariaDB ColumnStore extends MariaDB Server, a relational database for transaction processing, with distributed columnar storage and parallel query processing for scalable, high-performance analytical processing. This session helps MariaDB users understand how MariaDB ColumnStore works and why it’s needed for more demanding analytical workloads, and covers:
Use cases
Query processing
Bulk data insertion
Distributed partitions
Query optimization
Check out the webinar: https://imply.io/videos/whats-new-imply-3-3-apache-druid-0-18
The most recent Imply 3.3 release, based on Apache 0.18 brings several major new features, including joins, query laning and Clarity Alerts. These new features deliver increased design flexibility during design, and provide improved ingestion performance, and sub-second response times to help accelerate data warehouse and data lake deployments, and add real-time analytics in general.
Cortana Analytics Workshop: Predictive Maintenance in the IoT EraMSAdvAnalytics
Danielle Dean. Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Data-driven predictive maintenance, in particular, is gaining increasing attention in the industry along with the emerging demand of the Internet of Things (IoT) applications and the maturity of the supporting technologies. In this session we will present a real-world predictive maintenance example where the problem is formulated into three related questions via different machine learning models. A demonstration of how data flows through an end-to end-system, from ingesting the data to aggregating in real time to predicting based on historical data, will be done using tools such as Azure Machine Learning, Azure Stream Analytics, and Power BI. These technologies allow companies such as ThyssenKrupp Elevator to go from reactive to proactive and even predictive analysis of maintenance problems. Go to https://channel9.msdn.com/ to find the recording of this session.
Religious Minorities under Rising Discriminations and Violence in IndonesiaAndreas Harsono
In 2006, the Indonesian government introduced a new regulation called "religious harmony" and established the so-called Religious Harmony Forum as advisory body in all of its regencies, cities and provinces. It advises regents, mayors, and governors on religious affairs. Its basic principle is that the majority religion should protect the minorities and the minorities should respect the majority, meaning Islam. It triggered the rise of discrimination and violence against religious minorities in Indonesia.
Per Anhalter durch den Cloud Native Stack (extended edition)QAware GmbH
OOP 2017, München: Vortrag von Mario-Leander Reimer (@LeanderReimer, Cheftechnologe bei QAware).
Abstract: Cloud-Größen wie Google und Twitter haben die Kern-Bausteine ihrer Infrastruktur quelloffen verfügbar gemacht. Jeder kann nun selbst cloud-native Anwendungen entwickeln – Anwendungen, die in der Cloud zuverlässig laufen und fast beliebig skalieren. Dafür muss man sich seinen Cloud Native Stack zusammenstellen aus Bausteinen wie Docker, DC/OS, Kubernetes und Spring Cloud. Wir bereisen die wichtigsten Technologien und Architekturmuster rund um den Cloud Native Stack und bringen dabei eine Beispielanwendung schrittweise in der Cloud zum Laufen.
Zielpublikum: Architekten, Entwickler, Projektleiter, Entscheider, Cloud Native Nerds
Voraussetzungen: Grundkenntnisse in Java
Schwierigkeitsgrad: Anfänger
Extended Abstract: Jeder spricht über die Cloud, jeder ist in der Cloud. Dennoch scheinen die Akzeptanz und der Wille, die damit verbundenen Technologien im eigenen Unternehmen gewinnbringend einzusetzen, nach wie vor eher zögerlich. Dieser Vortrag soll dazu beitragen, die bestehenden Vorbehalte abzubauen. Wir werden nicht viele Worte zur Theorie von Cloud-nativen Anwendungen verschwenden. Stattdessen werden wir zeigen, dass der Cloud Native Stack reif ist für unternehmenskritische Anwendungen.
Madhav Kulkarni (Dow Chemicals, India)
Classification Codes in a patent document are given by Examiner. Due to time constraint and background/interest of the Examiner, some classification codes which could have been applied to the patent document are not applied. Though patent searchers know the inadequacy in searching Classification Codes, at times it is a useful field for focussed searches. Analysis of retrieved patent documents by Codes, Codes by Year and Codes by Assignee is typical but may not really represent the dataset truly. Here the presenter makes a case of Classification Codes and proposes solutions that would improve reliance on search and analysis of Classification Codes.
Understanding the architecture of MariaDB ColumnStoreMariaDB plc
MariaDB ColumnStore extends MariaDB Server, a relational database for transaction processing, with distributed columnar storage and parallel query processing for scalable, high-performance analytical processing. This session helps MariaDB users understand how MariaDB ColumnStore works and why it’s needed for more demanding analytical workloads, and covers:
Use cases
Query processing
Bulk data insertion
Distributed partitions
Query optimization
Check out the webinar: https://imply.io/videos/whats-new-imply-3-3-apache-druid-0-18
The most recent Imply 3.3 release, based on Apache 0.18 brings several major new features, including joins, query laning and Clarity Alerts. These new features deliver increased design flexibility during design, and provide improved ingestion performance, and sub-second response times to help accelerate data warehouse and data lake deployments, and add real-time analytics in general.
Cortana Analytics Workshop: Predictive Maintenance in the IoT EraMSAdvAnalytics
Danielle Dean. Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Data-driven predictive maintenance, in particular, is gaining increasing attention in the industry along with the emerging demand of the Internet of Things (IoT) applications and the maturity of the supporting technologies. In this session we will present a real-world predictive maintenance example where the problem is formulated into three related questions via different machine learning models. A demonstration of how data flows through an end-to end-system, from ingesting the data to aggregating in real time to predicting based on historical data, will be done using tools such as Azure Machine Learning, Azure Stream Analytics, and Power BI. These technologies allow companies such as ThyssenKrupp Elevator to go from reactive to proactive and even predictive analysis of maintenance problems. Go to https://channel9.msdn.com/ to find the recording of this session.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
5. ORDERS / INDEPENDENT DEMANDS
Order Data Car Configuration
Id, Order Time, … TYPE VARIANT UPHOLSTERY PAINT OPTION_1, OPTION_2, …
… …
… …
Customer‘s
orders
Predicted
Orders
Stock cars
Image: Energy Solutions International Inc
6. PARTS AND ABSTRACT DEMANDS
Id Description Attributes Terms
TR_1 Automatic transmission, supplier A … TYPE_1 OR
VAR_2 AND NOT OPT_2
…
TR_2 Automatic transmission, supplier B … TYPE_2 AND VAR_1 OR
OPT_5 OR OPT_6
DT_1 150hp, automatic transmission drive train …
LI_1 Xenon lights … OPT_3 OR VAR_9 OR …
BT_1 Small Battery … 8 <= OPT_3*200 + VAR_9*5 +
OPT_4*2 … <= 12
The constructive approach is impossible due to combinatory explosion.
The rules need to be evaluated during demand resolution.
7. Demand Resolver
WE NEED TO OVERCOME THREE CORE PROBLEMS…
Parts and
abstract demands
Orders /
Independent
Demands
Production
Planning
Analytics
BOMs /
Dependent
demands
8. Demand Resolver
1. HOW TO RESOLVE DEMANDS AS FAST AS POSSIBLE?
Parts and
abstract demands
Orders /
Independent
Demands
BOMs /
Dependent
demands
9. 2. HOW TO JOIN LARGE DATA SETS?
Parts and
abstract demands
Analytics
BOMs /
Dependent
demands
Orders /
Independent
Demands
10. 3. ANALYTICS ON LARGE DATA SETS?
Parts and
abstract demands
Analytics
BOMs /
Dependent
demands
Orders /
Independent
Demands
11. Demand Resolver
1. HOW TO RESOLVE DEMANDS AS FAST AS POSSIBLE?
Parts and
abstract demands
Orders /
Independent
Demands
On to problem #1…
BOMs /
Dependent
demands
14. 2 million parts
15 million abstract
demands
OR: ONLY CALCULATE WHAT IS STRICTLY NECESSARY
Step 2: REFINE
Refine the superset by
evaluating the usage terms in
O(n).
Actual demands
Post-
Filter
Pre-
Filter
Abstract
Demands
Step 1: FILTER
Pre-filter demands with a fast
index in O(log n).
Yields a superset of the
required parts.
~ 6000 possible
demands
~ 3000 actual demands
15. PRE-FILTER USING CANDIDATE AND A PROSCRIPTION SETS
■ Consider ((OPT_1) OR (OPT_2)) AND NOT(OPT_3 OR OPT_4)
■ Candidate set: C(t) = {OPT_1, OPT_2}
■ Proscription set: P(t) = {OPT_3, OPT_4}
■ Preselect by the criterium:
■ At least one condition from C(t) is satisfied
■ None of the conditions from P(t) are satisfied
16. EXAMPLE CANDIDATE AND PROSCRIPTION SETS
Candidate set Proscription set
A part can only be used together with
“white” or “blue” paint.
Entry: PART_1, C-PAINT:[white, blue]
Query: C-PAINT=blue
A part cannot be used with “yellow”
paint.
Entry: PART_2, P-PAINT:[yellow]
Query: NOT P-PAINT=yellow
20. 2. HOW TO JOIN TWO LARGE DATA SETS?
Parts and
abstract demands
Analytics
BOMs /
Dependend
demands
Orders /
Independent
Demands
let‘s continue with
problem #2…
21. FORWARD AND BACKWARD JOINS ON PARTS
Orders /
Independent Demands
Part Usages /
Dependend Demands
Parts /
Abstract Demands
Given: Order criteria (TYPE= XY12)
and parts criteria (PART_ID = 4711 OR 4712)
Task: Find all orders that satisfy the order criteria
AND contain at least one part, that satisifies the part criteria
Return all orders and the parts for each order that satisfy the part criteria.
Id … Id …Order Id Part Id
7.000.000
2.000.000
21.000.000.000
23. PARTIAL DENORMALIZATION TO THE RESCUE
Orders
<10 Mio. records instead of 21 billion
Ids Attributes Part Ids
1 … 1, 3, 786,…
2 … 2, 6, 786,…
3 .. 3, 75, 95, ..
Ids Attributes Order Ids
1 … 54, 23321, ..
2 … 1, 4, 8652, ..
3 … 864, 23452, …
Parts
24. SIMPLE, BUT NOT EASY: CUSTOM JOIN LOGIC IN SOLR
Step 1
Pre-filter the parts
using an index search
Step 2
Pre-filter the orders
using an index
search
Step 3
Compute the
intersection of both
supersets
All parts
Searched parts with
attribute A= XY
All orders
Orders to be built in Q4 with
attribute A
Intersect records
based on technical idsOrders to be built in
Q4
Final
Result
• Custom Search Components
• Solr Search Components
25. WE ONLY EXECUTE THE JOIN IF WE MUST
No filter Filter by order attributes Filter by part attributes Filter by order and part
attributes
Facets on parts Index JOIN Index JOIN
Facects on orders Index Index JOIN JOIN
List parts JOIN JOIN JOIN JOIN
List orders Index Index JOIN JOIN
Result sets may be unwieldingly large.
Early termination for interactive queries
if the result grows too large.
27. 3. ANALYTICS ON VERY LARGE DATA SETS?
Parts and
abstract demands
Analytics
BOMs /
Dependend
demands
Orders /
Independent
Demands
What about problem
#3?
28.
29. WHY APACHE SPARK
■ Fast
■ High-Level functional API
■ Well integrated
■ Batch semantics
■ Works well with Solr (If you use
your own connector…)
32. BULK EXPORT WITH A SHARED OFF-HEAP CACHE
Lucene Segment
Lucene Segment
Lucene Segment
Shared Off-Heap
Cache
1
DocId Order
DocId Part
Web
interface
2
3
Business Facade Object
data
4
http://chronicle.software/products/chronicle-map/
36. SOME OF OUR OPTIMIZATIONS
Bulk RequestHandler
Binary DocValue support
Boolean interpreter as postfilter
Mass data binary response format
Search components with
custom JOIN algorithm
Solving thousands of
orders with one request
Be able to store data
effective using our own
JOIN implementation.
Speed up the access to
persisted data dramatically
using binary doc values.
0111 0111
Use the standard Solr cinary
codec with an optimized data-
model that reduce the amount
of data by a factor of 8.
Computing
BOM
explosions
Enable Solr with custom post filters
to filter documents using stored bool
expessions .
37. LOW LEVEL OPTIMIZATIONS CAN YIELD GREAT RETURNS
October 14 January 15 May 15 October 15
4,9 ms
0,28 ms
24 ms
TimetocalculatetheBoMforoneorder
0,08 ms
Scoring (-8%)
Solr–Query Parser (-25%)
Stat-Cache (-8%)
Using String DocValues (-28%)
Development of the processing time Demand Calulation Service PoC Profiling result and the some improvements to reduce the query time.