Compelling Analytics: SQL Server Reporting Services in Dynamics AX 2009

本文探讨了Dynamics AX 2009如何集成SQL Server Reporting Services (SSRS),并展示了SSRS为报表和仪表板带来的强大功能及改进。通过对比原生MorphX报表引擎与SSRS,突出了SSRS在图表展示和数据可视化方面的优势。

转载:http://blogs.msdn.com/saveenr/archive/2008/09/06/compelling-analytics-sql-server-reporting-services-in-dynamics-ax-2009.aspx

Compelling Analytics: SQL Server Reporting Services in Dynamics AX 2009

In my role as Lead Program Manger for Business Intelligence in the Microsoft Dynamics AX team, one of my architectural goals is to help AX developers exploit the power of Microsoft's BI stack.

With regard to BI stacks they have a reporting component and Dynamics AX has its own reporting engine (MorphX) and in Dynamics AX 2009 we've added support and are integrating with SQL Server Reporting Services (SSRS).  From a strategic point of view this is important: Over time we want to not only work with it, but also increasingly use it in Dynamics. There's an huge team of talented people working on SSRS and it represents Microsoft's reporting story. By increasingly adopting this stack we leverage their expertise and efforts and simultaneously get more time back to focus on adding features to Dynamics AX.

So, from the perspective of code and managing resources (people and time and money) this makes sense. What I'll address in this post is to help make absolutely clear why this is a REALLY GOOD THING FOR USERS.

 

MORPHX reporting - the native reporting engine in AX

First, let's take a look at a report built with MorphX in Dynamics AX 2009.

NOTE: These screenshots use SQL 2005. Dynamics AX 2009 supports both SQL 2005 and the recently-released SQL 2008 but my demo machine only has 2005 installed on it.

We start with the report called "Divided Trial Balance" in the "General Ledger".

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Clicking on "Divided Trial Balance" will launch this dialog to set parameters for the report

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Select the From and To dates ...

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Click OK. The MorphX report is shown

 image

NOTE: I realize this is a bit hard read because the report is sized to fit the screen. Time for that 30" monitor.

AND NOW THE SSRS EQUIVALENT

If you paid close attention to the list of reports in General Ledger you may have noticed there were two reports called "Divided Trial Balance". One of them had an asterisk by the name.

In an out-of-the-box install of Dynamics AX 2009, reports built with SSRS are marked with the asterisk so that they are easy to find.

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Let's click on it. This window launches.

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It looks a bit different from the MorphX version but there are some common elements.

Set the From and To dates.

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And then click View Report. And you'll see this:

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So these reports look roughly equivalent (which was deliberate).

And you can see some features of SSRS in the export section of the header.

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AX ROLE CENTERS AND SSRS

AX 2009 Introduced Role Centers (a.k.a. Dashboards) for about different 30 user personas. Role Center's heavily use SSRS.

Let us look at a Role Center (by navigating to the Home tab).

My user account is set to be the CEO of the company in the AX database, so I see the "CEO Role Center" which I click Home.

NOTE: I don't have all the correct data in my AX demo database, so some of the numbers don't make sense in these screenshots (especially for the KPIs and Indicators)

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Everything bounded by the red box below is implemented as a SharePoint web page.

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And everything you see highlighted by the red boxes below are actually SSRS reports (also web pages) embedded in the Role Center web page.

 image

Role Centers are a key feature of Dynamics AX 2009 and SSRS is critical for them to work. If you don't use SSRS then you won't achieve the value we put into the product.

To make this more obvious - here's what happens if you don't have SSRS running.

image

Again: you need to be using SSRS with Dynamics AX 2009.

Now, let's take a look at some of the other role centers. It should be obvious where SSRS is being used.

 

Bookkeeper role center

image 

 

The Operation Manager role center

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The Production Manager role center

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Shipping and Receiving role center

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At this point what you've seen is that SSRS gives us some charting abilities which were not possible with MorphX.

 

THE PAYOFF - THE FUTURE WITH SSRS LOOKS GREAT

But, where can you take SSRS technology? How much better can these reports be because of SSRS? Because we didn't spend a much time making the reports "slick" in AX2009 it's sometimes hard to see the value. Fortunately, I know exactly how to show you were we want to go with SSRS and what is possible TODAY.

Here are real-world examples from my previous team, Microsoft Forefront Client Security. This product was released in 2007 and uses plain SSRS 2005 with no Dundas Charts or special activex controls. Everything you see is possible with "what's in the box".

snap0292  snap0293 snap0295 snap0296

 snap0297 snap0300 snap0302

 

And let's take a close up view on the Security Summary report.

 

image

 

Now, wouldn't you like that in some of your dashboards?

 

Or if you need more of the data look at the security state assessment summary report

 

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I hope this post has made you more familiar with Dynamics AX and some of the new features in Dynamics AX 2009 and most importantly I hope you can see that we aren't interested in SSRS just because that's what are paying some "strategic tax" but rather there's some really great value in SSRS for AX customers.

Over time, we will increasingly adopt and integrate SSRS into AX. And in our next release after AX 209 I am planning on giving you some compelling analytics experiences both in terms of the insight they offer for BI as well as in terms of visual style and quality by fully exploiting the power of SSRS.

 

 

```markdown Below are the **formal English reviewer comments** on the manuscript titled *"A Robust Framework for Coffee Bean Package Label Recognition: Integrating Image Enhancement with Vision-Language OCR Models"*. These comments follow academic peer-review standards and are suitable for submission to a journal or conference. --- ### **General Comments** The paper presents a novel OCR framework tailored for coffee bean package label recognition, integrating image enhancement techniques with vision-language models (VLMs), particularly Qwen-VL variants guided by structured prompts. The authors contribute two curated datasets (LRCB and HRCB), propose a multi-branch preprocessing pipeline, and conduct extensive evaluations against established OCR baselines using multiple metrics. The work addresses a practical and increasingly relevant problem in food product digitization, with implications for supply chain tracking, brand verification, and retail automation. Overall, the manuscript is well-structured, technically sound, and experimentally thorough. The inclusion of real-world challenges—such as low resolution, variable lighting, rotation, and packaging materials—strengthens the relevance of the study. However, several aspects require clarification or strengthening before publication. While the core idea is promising, the novelty should be more precisely positioned relative to existing product-focused OCR research. With appropriate revisions—particularly regarding **method transparency**, **ablation studies**, and **data annotation details**—this work would make a solid contribution to the field of document image analysis and industrial AI applications. --- ### **Specific Comments** 1. **Contribution Positioning Needs Refinement** The claim that this work fills a "significant gap" in branded OCR frameworks for coffee packaging is somewhat overstated. While domain-specific optimization is valuable, similar approaches have been explored in broader product label recognition (e.g., POIE dataset, DocBank). Please reframe the contribution to emphasize *domain-adapted integration* rather than asserting a complete absence of prior art. 2. **Insufficient Detail on Annotation Protocol** The paper mentions manual transcription and bounding box labeling but lacks critical details: Is the annotation at the word, line, or paragraph level? Were character-level alignments performed? What inter-annotator agreement metrics were used (e.g., IoU, F1)? Without this information, the reliability of ground truth remains unclear. Please add a subsection detailing the annotation schema and quality control procedures. 3. **Lack of Ablation Study for Preprocessing Components** The three-path preprocessing strategy (Standard Enhanced, High Contrast, Grayscale-Based) is intuitively justified, but its individual impact is not quantitatively assessed. For instance, what is the performance drop when skipping each branch? Does combining all three consistently improve results over single-path inputs? An ablation study is essential to validate the necessity of the full pipeline. 4. **Ambiguity in Exact Match Metric Definition** The Exact Match (EM) metric is reported without specifying whether text normalization (e.g., lowercasing, whitespace trimming, punctuation handling) was applied prior to comparison. Given that OCR outputs often differ only in formatting (e.g., “100g” vs “100 g”), the raw EM score may underestimate true accuracy. Please explicitly define the text normalization rules used, if any. 5. **Misleading Model Naming Scheme** The naming convention “CB-OCR-*” (e.g., CB-OCR-RolmOCR, CB-OCR-Qwen2-VL) suggests that these are newly proposed architectures. In reality, they are fine-tuned instances of existing models. This could mislead readers into overestimating architectural innovation. Consider renaming them to reflect their origins, such as “Fine-tuned Qwen2-VL-OCR-2B-Instruct (Prompted)”. 6. **Need for Prompt Engineering Analysis** The use of structured prompts is highlighted as a key component, yet no comparison between different prompt designs is provided (e.g., zero-shot vs. few-shot, general vs. domain-specific wording). Did the authors perform prompt sensitivity analysis? Which prompt yielded the best performance? Including such analysis would strengthen the validity of the prompt-based approach. 7. **Questionable Baseline Performance in Some Cases** In Table 3, DocTR fails to detect any text on the HRCB dataset (marked as “—”), while Tesseract takes over 13 seconds on POIE. These outliers need explanation: Was it due to memory overflow, improper configuration, or failure in text localization? Please confirm the experimental setup (batch size, input resolution, GPU memory) to ensure fair and reproducible comparisons. 8. **Computational Efficiency Should Inform Recommendations** The timing analysis (Figures 6 and 7) effectively highlights the trade-off between accuracy and speed. CB-OCR-RolmOCR achieves high scores but suffers in latency (up to 13.53s), whereas CB-OCR-Nanonets is significantly faster. The authors should leverage these findings to provide practical deployment guidelines—e.g., recommending model choices based on application requirements (real-time scanning vs. offline archiving). 9. **Clarify Dynamic Model Loading Mechanism** Section 3.3 mentions a “dynamic model loading mechanism,” but the implementation details are absent. Is model selection automated based on input characteristics? Or is it user-configurable? Please clarify how this functionality operates within the framework and whether it incurs additional overhead. --- ### **Minor Comments** - Line 367: Typo – “Extract All visible text” → “Extract all visible text” - Reference [11]: “RolmOCR” URL is listed as “Not specified.” Please provide a valid link or note if it is private. - Figure captions could be more descriptive (e.g., specify brands or text content in sample images). - Consider releasing the preprocessing code alongside the dataset for full reproducibility. --- ### **Recommendation** **Minor Revision** The manuscript presents a compelling application of vision-language models to a niche but impactful domain. With targeted clarifications and supplementary experiments (especially ablation and prompt analysis), the paper will be suitable for publication. Please address all concerns above and resubmit with a point-by-point response letter indicating how each comment has been handled. ```
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