Respan Dataset Explorer

System: You are an expert Named Entity Recognition (NER) system. Your task is to extract all the company names mentioned in the provided text.
Company Name: The name of the company or corporation as written in the article (e.g., "Apple Inc.", "Microsoft").
Example Input :Apple has been investing rapidly in AI during the last couple of years, competing with Microsoft and Tesla Inc (TSLA) over market shares.
Company Name: Apple; Microsoft; Tesla Inc;
Provide a correct and a wrong answer.
User: This is CNBC's live blog covering Asia-Pacific markets. Shares in the Asia-Pacific traded mixed on Thursday after Wednesday's negative session, with stocks in Shenzhen and Shanghai falling sharply. Mainland China's Shenzhen Component fell 2.105% to 11,526.96, dragged down by energy stocks. The Shanghai Composite shed 1.16% to 3,199.92, while the Hang Seng index in Hong Kong added 0.44% to 18930.38. The Nikkei 225 in Japan rose 0.21% to 27,875.91 and the Topix index was 0.15% higher at 1,950.43. The Japanese yen was last trading at 143.69 against the dollar after a reported "rate check" by the Bank of Japan . In South Korea, the Kospi closed 0.4% lower at 2,401.83 and Australia's S&P/ASX 200 gained 0.21% to 6,842.90. In China, the central bank kept its one-year medium-term lending facility (MLF) unchanged at 2.75%, as expected. Australia's unemployment rate for August came in at 3.5%, slightly higher than July. U.S. indexes inched higher overnight and the producer price index showed a decrease in wholesale prices of 0.1% in August amid inflation fears. TITLE: Chinese yuan could weaken past 7 against the dollar by year-end, says Credit Suisse The Chinese yuan could weaken to 7.05 against the U.S. dollar by year-end, said
Assistant:

Correct Answer: Credit Suisse

Wrong Answer: Shenzhen; Shanghai Composite

System: You are an expert Named Entity Recognition (NER) system. Your task is to extract all the company names mentioned in the provided text.
Company Name: The name of the company or corporation as written in the article (e.g., "Apple Inc.", "Microsoft").
Example Input :Apple has been investing rapidly in AI during the last couple of years, competing with Microsoft and Tesla Inc (TSLA) over market shares.
Company Name: Apple; Microsoft; Tesla Inc;
Provide a correct and a wrong answer.
User: This is CNBC's live blog covering Asia-Pacific markets. Shares in the Asia-Pacific traded mixed on Thursday after Wednesday's negative session, with stocks in Shenzhen and Shanghai falling sharply. Mainland China's Shenzhen Component fell 2.105% to 11,526.96, dragged down by energy stocks. The Shanghai Composite shed 1.16% to 3,199.92, while the Hang Seng index in Hong Kong added 0.44% to 18930.38. The Nikkei 225 in Japan rose 0.21% to 27,875.91 and the Topix index was 0.15% higher at 1,950.43. The Japanese yen was last trading at 143.69 against the dollar after a reported "rate check" by the Bank of Japan . In South Korea, the Kospi closed 0.4% lower at 2,401.83 and Australia's S&P/ASX 200 gained 0.21% to 6,842.90. In China, the central bank kept its one-year medium-term lending facility (MLF) unchanged at 2.75%, as expected. Australia's unemployment rate for August came in at 3.5%, slightly higher than July. U.S. indexes inched higher overnight and the producer price index showed a decrease in wholesale prices of 0.1% in August amid inflation fears. TITLE: Chinese yuan could weaken past 7 against the dollar by year-end, says Credit Suisse The Chinese yuan could weaken to 7.05 against the U.S. dollar by year-end, said
Assistant:

Correct Answer: Credit Suisse

Wrong Answer: Bank of Japan

You are a database expert and SQL optimizer. You will be given a SQL query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence. Your task is to create a new pair of original and rewritten queries that invoke a similar rewrite.

[ORIGINAL QUERY]```select a.ca_state state,count(*) cnt from customer_address a,customer c,store_sales s,date_dim d,item i where a.ca_address_sk = c.c_current_addr_sk and c.c_customer_sk = s.ss_customer_sk and s.ss_sold_date_sk = d.d_date_sk and s.ss_item_sk = i.i_item_sk and d.d_month_seq = (select distinct (d_month_seq) from date_dim where d_year = 2002 and d_moy = 3 ) and i.i_current_price > 1.2 * (select avg(j.i_current_price) from item j where j.i_category = i.i_category) group by a.ca_state having count(*) >= 10 order by cnt,a.ca_state limit 100;```

[REWRITTEN QUERY]```WITH avg_price AS ( SELECT i_category, AVG(i_current_price) avg_price FROM item GROUP BY i_category ), month_seq AS ( SELECT d_month_seq FROM date_dim WHERE d_year = 2002 AND d_moy = 3 LIMIT 1 ) SELECT a.ca_state state, COUNT(*) cnt FROM customer_address a JOIN customer c ON a.ca_address_sk = c.c_current_addr_sk JOIN store_sales s ON c.c_customer_sk = s.ss_customer_sk JOIN date_dim d ON s.ss_sold_date_sk = d.d_date_sk JOIN item i ON s.ss_item_sk = i.i_item_sk JOIN avg_price ap ON i.i_category = ap.i_category JOIN month_seq ms ON d.d_month_seq = ms.d_month_seq WHERE i.i_current_price > 1.2 * ap.avg_price GROUP BY a.ca_state HAVING COUNT(*) >= 10 ORDER BY cnt, a.ca_state LIMIT 100;```

Now, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation is similar to the provided demonstration.Keep the following points in mind while creating the new query pair:
1. The original query must not have any CTEs.
2. The original query must make use of the following fact tables: ['store_sales']. Below is the join graph for these fact tables:
    ['store_sales(ss_cdemo_sk) references customer_demographics(cd_demo_sk);', 'store_sales(ss_customer_sk) references customer(c_customer_sk);', 'store_sales(ss_sold_time_sk) references time_dim(t_time_sk);']
4. The original query must employ 2 joins.
5. The original query must employ the following aggregate functions: ['count'].
6. The original query must employ the following operators in its WHERE clause: ['min'].
7. The rewritten query must be equivalent to the original query, and must follow a similar rewrite pattern as in the given example. Enclose the original and rewritten queries within ``` and ```.

[ORIGINAL QUERY]
```sql
SELECT c.c_customer_id customer_id, COUNT(*) total_purchases
FROM store_sales s, customer c, time_dim t
WHERE s.ss_customer_sk = c.c_customer_sk
AND s.ss_sold_time_sk = t.t_time_sk
AND s.ss_quantity > (
  SELECT MIN(ss_quantity)
  FROM store_sales ss
  WHERE ss.ss_sold_time_sk = t.t_time_sk
)
GROUP BY c.c_customer_id
HAVING COUNT(*) > 5
ORDER BY total_purchases DESC, c.c_customer_id
LIMIT 50;
```

[REWRITTEN QUERY]
```sql
WITH min_quantity AS (
  SELECT t_time_sk, MIN(ss_quantity) min_qty
  FROM store_sales
  GROUP BY t_time_sk
)
SELECT c.c_customer_id customer_id, COUNT(*) total_purchases
FROM store_sales s
JOIN customer c ON s.ss_customer_sk = c.c_customer_sk
JOIN time_dim t ON s.ss_sold_time_sk = t.t_time_sk
JOIN min_quantity mq ON s.ss_sold_time_sk = mq.t_time_sk
WHERE s.ss_quantity > mq.min_qty
GROUP BY c.c_customer_id
HAVING COUNT(*) > 5
ORDER BY total_purchases DESC, c.c_customer_id
LIMIT 50;
```

In this rewritten query, the use of a common table expression (CTE) `min_quantity` allows for the precomputation of minimum quantities grouped by time key (`t_time_sk`). This transformation simplifies the main query by avoiding a subquery in the `WHERE` clause, thus optimizing performance.

You are a database expert and SQL optimizer. You will be given a SQL query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence. Your task is to create a new pair of original and rewritten queries that invoke a similar rewrite.

[ORIGINAL QUERY]```select a.ca_state state,count(*) cnt from customer_address a,customer c,store_sales s,date_dim d,item i where a.ca_address_sk = c.c_current_addr_sk and c.c_customer_sk = s.ss_customer_sk and s.ss_sold_date_sk = d.d_date_sk and s.ss_item_sk = i.i_item_sk and d.d_month_seq = (select distinct (d_month_seq) from date_dim where d_year = 2002 and d_moy = 3 ) and i.i_current_price > 1.2 * (select avg(j.i_current_price) from item j where j.i_category = i.i_category) group by a.ca_state having count(*) >= 10 order by cnt,a.ca_state limit 100;```

[REWRITTEN QUERY]```WITH avg_price AS ( SELECT i_category, AVG(i_current_price) avg_price FROM item GROUP BY i_category ), month_seq AS ( SELECT d_month_seq FROM date_dim WHERE d_year = 2002 AND d_moy = 3 LIMIT 1 ) SELECT a.ca_state state, COUNT(*) cnt FROM customer_address a JOIN customer c ON a.ca_address_sk = c.c_current_addr_sk JOIN store_sales s ON c.c_customer_sk = s.ss_customer_sk JOIN date_dim d ON s.ss_sold_date_sk = d.d_date_sk JOIN item i ON s.ss_item_sk = i.i_item_sk JOIN avg_price ap ON i.i_category = ap.i_category JOIN month_seq ms ON d.d_month_seq = ms.d_month_seq WHERE i.i_current_price > 1.2 * ap.avg_price GROUP BY a.ca_state HAVING COUNT(*) >= 10 ORDER BY cnt, a.ca_state LIMIT 100;```

Now, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation is similar to the provided demonstration.Keep the following points in mind while creating the new query pair:
1. The original query must not have any CTEs.
2. The original query must make use of the following fact tables: ['catalog_returns', 'web_sales']. Below is the join graph for these fact tables:
    ['catalog_returns(cr_refunded_cdemo_sk) references customer_demographics(cd_demo_sk);', 'catalog_returns(cr_reason_sk) references reason(r_reason_sk);', 'catalog_returns(cr_call_center_sk) references call_center(cc_call_center_sk);']
    ['web_sales(ws_bill_cdemo_sk) references customer_demographics(cd_demo_sk);', 'web_sales(ws_bill_customer_sk) references customer(c_customer_sk);', 'web_sales(ws_sold_time_sk) references time_dim(t_time_sk);']
5. The original query must employ 3 joins.
6. The original query must employ the following aggregate functions: ['sum', 'avg'].
7. The original query must employ the following operators in its WHERE clause: ['max'].
8. The rewritten query must be equivalent to the original query, and must follow a similar rewrite pattern as in the given example. Enclose the original and rewritten queries within ``` and ```.

Here is a new pair of queries based on the requirements and following a similar rewrite pattern as provided:

[ORIGINAL QUERY]
```sql
SELECT cd.cd_gender, SUM(cr.cr_refunded_amt) total_refunded
FROM catalog_returns cr, customer_demographics cd, reason r
WHERE cr.cr_refunded_cdemo_sk = cd.cd_demo_sk
  AND cr.cr_reason_sk = r.r_reason_sk
  AND cr.cr_return_amt > (SELECT MAX(cr2.cr_return_amt) 
                          FROM catalog_returns cr2 
                          WHERE cr2.cr_call_center_sk = cr.cr_call_center_sk)
GROUP BY cd.cd_gender
HAVING SUM(cr.cr_refunded_amt) > (SELECT AVG(cr3.cr_refunded_amt) 
                                  FROM catalog_returns cr3)
ORDER BY total_refunded DESC;
```

[REWRITTEN QUERY]
```sql
WITH max_return_amt AS (
  SELECT cr_return_amt, cr_call_center_sk 
  FROM catalog_returns 
  WHERE cr_return_amt = (
    SELECT MAX(cr_return_amt) 
    FROM catalog_returns cr2 
    WHERE cr2.cr_call_center_sk = catalog_returns.cr_call_center_sk)
),
avg_refunded_amt AS (
  SELECT AVG(cr_refunded_amt) avg_amt 
  FROM catalog_returns
)
SELECT cd.cd_gender, SUM(cr.cr_refunded_amt) total_refunded
FROM catalog_returns cr
JOIN customer_demographics cd ON cr.cr_refunded_cdemo_sk = cd.cd_demo_sk
JOIN reason r ON cr.cr_reason_sk = r.r_reason_sk
JOIN max_return_amt mra ON cr.cr_return_amt = mra.cr_return_amt
CROSS JOIN avg_refunded_amt ara
GROUP BY cd.cd_gender
HAVING SUM(cr.cr_refunded_amt) > ara.avg_amt
ORDER BY total_refunded DESC;
```

Explanation:
- In the original query, we directly use nested subqueries to determine the maximum return amount and average refunded amount, which leads to potentially multiple recalculations.
- In the rewritten query, we employ Common Table Expressions (CTEs), `max_return_amt` and `avg_refunded_amt`, to pre-calculate these values and join against them, improving clarity and potentially optimizing repeated computations.
- The same logic is maintained in the performance-optimized form by using these CTEs, ensuring equivalent functionality between both queries.

You are a database expert and SQL optimizer. You will be given a SQL query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence. Your task is to create a new pair of original and rewritten queries that invoke a similar rewrite.

[ORIGINAL QUERY]```select a.ca_state state,count(*) cnt from customer_address a,customer c,store_sales s,date_dim d,item i where a.ca_address_sk = c.c_current_addr_sk and c.c_customer_sk = s.ss_customer_sk and s.ss_sold_date_sk = d.d_date_sk and s.ss_item_sk = i.i_item_sk and d.d_month_seq = (select distinct (d_month_seq) from date_dim where d_year = 2002 and d_moy = 3 ) and i.i_current_price > 1.2 * (select avg(j.i_current_price) from item j where j.i_category = i.i_category) group by a.ca_state having count(*) >= 10 order by cnt,a.ca_state limit 100;```

[REWRITTEN QUERY]```WITH avg_price AS ( SELECT i_category, AVG(i_current_price) avg_price FROM item GROUP BY i_category ), month_seq AS ( SELECT d_month_seq FROM date_dim WHERE d_year = 2002 AND d_moy = 3 LIMIT 1 ) SELECT a.ca_state state, COUNT(*) cnt FROM customer_address a JOIN customer c ON a.ca_address_sk = c.c_current_addr_sk JOIN store_sales s ON c.c_customer_sk = s.ss_customer_sk JOIN date_dim d ON s.ss_sold_date_sk = d.d_date_sk JOIN item i ON s.ss_item_sk = i.i_item_sk JOIN avg_price ap ON i.i_category = ap.i_category JOIN month_seq ms ON d.d_month_seq = ms.d_month_seq WHERE i.i_current_price > 1.2 * ap.avg_price GROUP BY a.ca_state HAVING COUNT(*) >= 10 ORDER BY cnt, a.ca_state LIMIT 100;```

Now, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation is similar to the provided demonstration.Keep the following points in mind while creating the new query pair:
1. The original query must contain CTEs.
2. The original query must make use of the following fact tables: ['web_sales']. Below is the join graph for these fact tables:
    ['web_sales(ws_bill_cdemo_sk) references customer_demographics(cd_demo_sk);', 'web_sales(ws_promo_sk) references promotion(p_promo_sk);', 'web_sales(ws_ship_date_sk) references date_dim(d_date_sk);']
4. The original query must employ 0 joins.
5. The original query must employ the following operators in its WHERE clause: [].
6. The rewritten query must be equivalent to the original query, and must follow a similar rewrite pattern as in the given example. Enclose the original and rewritten queries within ``` and ```.

Here is a new pair of original and rewritten queries based on the criteria and the rewriting pattern demonstrated:

[ORIGINAL QUERY]
```sql
WITH promo_avg_sales AS (
    SELECT ws_promo_sk, AVG(ws_net_paid) avg_net_paid
    FROM web_sales
    GROUP BY ws_promo_sk
),
max_ship_date AS (
    SELECT MAX(d_date) max_date
    FROM date_dim
    WHERE d_year = 2001
)
SELECT ws_promo_sk, SUM(ws_net_paid) total_sales
FROM web_sales, promo_avg_sales, max_ship_date
WHERE ws_promo_sk = promo_avg_sales.ws_promo_sk
    AND ws_ship_date_sk = (SELECT d_date_sk FROM date_dim WHERE d_date = max_ship_date.max_date)
    AND ws_net_paid > promo_avg_sales.avg_net_paid
GROUP BY ws_promo_sk
HAVING SUM(ws_net_paid) > 1000
ORDER BY total_sales DESC;
```

[REWRITTEN QUERY]
```sql
WITH promo_avg_sales AS (
    SELECT ws_promo_sk, AVG(ws_net_paid) avg_net_paid
    FROM web_sales
    GROUP BY ws_promo_sk
),
max_ship_date AS (
    SELECT d_date_sk
    FROM date_dim
    WHERE d_date = (SELECT MAX(d_date) FROM date_dim WHERE d_year = 2001)
)
SELECT ws.ws_promo_sk, SUM(ws.ws_net_paid) total_sales
FROM web_sales ws
JOIN promo_avg_sales pas ON ws.ws_promo_sk = pas.ws_promo_sk
JOIN max_ship_date msd ON ws.ws_ship_date_sk = msd.d_date_sk
WHERE ws.ws_net_paid > pas.avg_net_paid
GROUP BY ws.ws_promo_sk
HAVING SUM(ws.ws_net_paid) > 1000
ORDER BY total_sales DESC;
```

### Explanation:
- The `ORIGINAL QUERY` uses Common Table Expressions (CTEs) to calculate the average `ws_net_paid` for each `ws_promo_sk` and find the maximum shipping date in the year 2001. It then selects the `ws_promo_sk` and total sales (`ws_net_paid`) for those sales exceeding the average within the specified maximum date, using joins and a subquery.
  
- The `REWRITTEN QUERY` reforms the query by explicitly joining the `web_sales` table with the derived CTE results (`promo_avg_sales` and `max_ship_date`) to avoid the implicit cartesian product and separate subquery joins, mirroring the refactoring demonstrated in the original provided example. This structure enhances clarity while maintaining query equivalence.

User: Rewrite this text a bit in brief, don't loose any key facts or add your own:

Migrant rights activists have called on the UK Home Office to abandon the Identify and Prioritize Immigration Cases system, as an AI-based algorithm is involved in shaping legal decisions, including the return of people to their home countries.. IPIC is one of several artificial intelligence programs that are being implemented by UK government agencies. Thus, the government is striving for greater speed and efficiency of processes in a wide variety of areas — from healthcare to social security. The Interior Ministry uses IPIC to "assess the likelihood of expelling applicants and ranks the harm caused by immigration offenders, as well as automates the process of prioritizing cases." Privacy International has voiced concerns that officials will "stamp out" the recommendations of the algorithm regarding actions in the case, because it is much easier than critically reviewing the case. The authorities insist that people are still responsible for all decisions, and every recommendation made in the system is reviewed by an employee who weighs all the factors proposed by the program.

Migrant rights activists are urging the UK Home Office to discontinue the Identify and Prioritize Immigration Cases (IPIC) system, which utilizes an AI algorithm to influence legal decisions, including deportations. IPIC is one of several AI programs adopted by UK government agencies to improve efficiency in areas such as healthcare and social security. The Interior Ministry uses IPIC to assess the likelihood of expelling applicants, rank the harm caused by immigration offenders, and automate case prioritization. Privacy International has raised concerns that officials may uncritically follow the algorithm's recommendations instead of thoroughly reviewing each case. In response, authorities assert that all system recommendations are reviewed by employees who evaluate all relevant factors, maintaining human responsibility in decision-making.

You are a database expert and SQL optimizer. You will be given a SQL query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence. Your task is to create a new pair of original and rewritten queries that invoke a similar rewrite.

[ORIGINAL QUERY]```select a.ca_state state,count(*) cnt from customer_address a,customer c,store_sales s,date_dim d,item i where a.ca_address_sk = c.c_current_addr_sk and c.c_customer_sk = s.ss_customer_sk and s.ss_sold_date_sk = d.d_date_sk and s.ss_item_sk = i.i_item_sk and d.d_month_seq = (select distinct (d_month_seq) from date_dim where d_year = 2002 and d_moy = 3 ) and i.i_current_price > 1.2 * (select avg(j.i_current_price) from item j where j.i_category = i.i_category) group by a.ca_state having count(*) >= 10 order by cnt,a.ca_state limit 100;```

[REWRITTEN QUERY]```WITH avg_price AS ( SELECT i_category, AVG(i_current_price) avg_price FROM item GROUP BY i_category ), month_seq AS ( SELECT d_month_seq FROM date_dim WHERE d_year = 2002 AND d_moy = 3 LIMIT 1 ) SELECT a.ca_state state, COUNT(*) cnt FROM customer_address a JOIN customer c ON a.ca_address_sk = c.c_current_addr_sk JOIN store_sales s ON c.c_customer_sk = s.ss_customer_sk JOIN date_dim d ON s.ss_sold_date_sk = d.d_date_sk JOIN item i ON s.ss_item_sk = i.i_item_sk JOIN avg_price ap ON i.i_category = ap.i_category JOIN month_seq ms ON d.d_month_seq = ms.d_month_seq WHERE i.i_current_price > 1.2 * ap.avg_price GROUP BY a.ca_state HAVING COUNT(*) >= 10 ORDER BY cnt, a.ca_state LIMIT 100;```

Now, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation is similar to the provided demonstration.Keep the following points in mind while creating the new query pair:
1. The original query must not have any CTEs.
2. The original query must make use of the following fact tables: ['store_returns']. Below is the join graph for these fact tables:
    ['store_returns(sr_item_sk) references item(i_item_sk);', 'store_returns(sr_cdemo_sk) references customer_demographics(cd_demo_sk);', 'store_returns(sr_addr_sk) references customer_address(ca_address_sk);']
4. The original query must employ 3 joins.
5. The original query must employ the following operators in its WHERE clause: [].
6. The rewritten query must be equivalent to the original query, and must follow a similar rewrite pattern as in the given example. Enclose the original and rewritten queries within ``` and ```.

Here is a new pair of original and rewritten SQL queries following the specified constraints and similar to the provided transformation:

[ORIGINAL QUERY]
```sql
SELECT sr_return_quantity, 
       COUNT(*) AS cnt 
FROM store_returns sr, 
     item i, 
     customer_demographics cd 
WHERE sr.sr_item_sk = i.i_item_sk 
  AND sr.sr_cdemo_sk = cd.cd_demo_sk 
  AND sr.sr_addr_sk = (SELECT ca_address_sk 
                       FROM customer_address 
                       WHERE ca_city = 'Seattle') 
  AND i.i_color = 'red' 
  AND cd.cd_gender = 'M' 
GROUP BY sr_return_quantity 
ORDER BY cnt DESC, sr_return_quantity 
LIMIT 50;
```

[REWRITTEN QUERY]
```sql
WITH seattle_address AS ( 
    SELECT ca_address_sk 
    FROM customer_address 
    WHERE ca_city = 'Seattle' 
) 
SELECT sr_return_quantity, 
       COUNT(*) AS cnt 
FROM store_returns sr 
JOIN item i ON sr.sr_item_sk = i.i_item_sk 
JOIN customer_demographics cd ON sr.sr_cdemo_sk = cd.cd_demo_sk 
JOIN seattle_address sa ON sr.sr_addr_sk = sa.ca_address_sk 
WHERE i.i_color = 'red' 
  AND cd.cd_gender = 'M' 
GROUP BY sr_return_quantity 
ORDER BY cnt DESC, sr_return_quantity 
LIMIT 50;
```

### Explanation
- The original query uses an inline subquery to filter the `sr.sr_addr_sk` based on the address city 'Seattle'.
- In the rewritten query, we use a Common Table Expression (CTE) named `seattle_address` to encapsulate the filter logic for `ca_address_sk`, making the main query cleaner and potentially aiding in optimization.
- Both queries achieve the same result, but the rewritten version leverages modern SQL practices, such as explicit `JOIN` syntax and CTEs, to improve readability and possibly performance.

You are a database expert and SQL optimizer. You will be given a SQL query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence. Your task is to create a new pair of original and rewritten queries that invoke a similar rewrite.

[ORIGINAL QUERY]```select a.ca_state state,count(*) cnt from customer_address a,customer c,store_sales s,date_dim d,item i where a.ca_address_sk = c.c_current_addr_sk and c.c_customer_sk = s.ss_customer_sk and s.ss_sold_date_sk = d.d_date_sk and s.ss_item_sk = i.i_item_sk and d.d_month_seq = (select distinct (d_month_seq) from date_dim where d_year = 2002 and d_moy = 3 ) and i.i_current_price > 1.2 * (select avg(j.i_current_price) from item j where j.i_category = i.i_category) group by a.ca_state having count(*) >= 10 order by cnt,a.ca_state limit 100;```

[REWRITTEN QUERY]```WITH avg_price AS ( SELECT i_category, AVG(i_current_price) avg_price FROM item GROUP BY i_category ), month_seq AS ( SELECT d_month_seq FROM date_dim WHERE d_year = 2002 AND d_moy = 3 LIMIT 1 ) SELECT a.ca_state state, COUNT(*) cnt FROM customer_address a JOIN customer c ON a.ca_address_sk = c.c_current_addr_sk JOIN store_sales s ON c.c_customer_sk = s.ss_customer_sk JOIN date_dim d ON s.ss_sold_date_sk = d.d_date_sk JOIN item i ON s.ss_item_sk = i.i_item_sk JOIN avg_price ap ON i.i_category = ap.i_category JOIN month_seq ms ON d.d_month_seq = ms.d_month_seq WHERE i.i_current_price > 1.2 * ap.avg_price GROUP BY a.ca_state HAVING COUNT(*) >= 10 ORDER BY cnt, a.ca_state LIMIT 100;```

Now, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation is similar to the provided demonstration.Keep the following points in mind while creating the new query pair:
1. The original query must not have any CTEs.
2. The original query must make use of the following fact tables: ['web_sales']. Below is the join graph for these fact tables:
    ['web_sales(ws_web_site_sk) references web_site(web_site_sk);', 'web_sales(ws_ship_hdemo_sk) references household_demographics(hd_demo_sk);', 'web_sales(ws_item_sk) references item(i_item_sk);']
4. The original query must employ 0 joins.
5. The original query must employ the following operators in its WHERE clause: [].
6. The rewritten query must be equivalent to the original query, and must follow a similar rewrite pattern as in the given example. Enclose the original and rewritten queries within ``` and ```.

Certainly! Here is a new pair of original and rewritten queries following similar rewrite logic:

[ORIGINAL QUERY]
```sql
SELECT ws_item_sk, SUM(ws_quantity) AS total_quantity
FROM web_sales
WHERE ws_item_sk IN (
    SELECT i_item_sk
    FROM item
    WHERE i_class = (SELECT i_class FROM item WHERE i_item_id = 'ITEM12345')
)
GROUP BY ws_item_sk
HAVING SUM(ws_quantity) > 500
ORDER BY total_quantity DESC
LIMIT 50;
```

[REWRITTEN QUERY]
```sql
WITH item_class AS (
    SELECT i_item_sk 
    FROM item 
    WHERE i_class = (SELECT i_class FROM item WHERE i_item_id = 'ITEM12345')
)
SELECT ws_item_sk, SUM(ws_quantity) AS total_quantity
FROM web_sales
JOIN item_class ic ON ws_item_sk = ic.i_item_sk
GROUP BY ws_item_sk
HAVING SUM(ws_quantity) > 500
ORDER BY total_quantity DESC
LIMIT 50;
```

**Explanation of the Rewrite:**

- **Original Query:**
  - Uses a subquery to filter `ws_item_sk` values based on `i_class`. 
  - Gets the `i_class` for a specific item with `i_item_id = 'ITEM12345'` and finds other items within the same class.
  - Performs aggregation on `ws_quantity` from `web_sales`.

- **Rewritten Query:**
  - Introduces a CTE `item_class` to pre-calculate the item keys of interest.
  - This allows simplifying and potentially optimizing the main query by having a direct join rather than relying on an IN subquery filter.
  - Equivalent logic is maintained via CTE leading to a potential increase in performance by pushing down the filtering logic.

relationship is basically a combination of numbers, symbols, operators that show the connection between quantities.<instructions>verify my knowledge. add yes/no. tell the correctness in percent. point out what are right, what are wrong, and provided correct statement at the end.</instructions>

Yes, your statement is partially correct. I would estimate its correctness at about 70%. 

**What is right:**
- You correctly mention that relationships involve *numbers, symbols, and operators*.
- You acknowledge that relationships *show the connection between quantities*.

**What is wrong:**
- The definition is somewhat broad and lacks specificity.
- A relationship in mathematics isn't just any combination of numbers, symbols, and operators; it specifically refers to how variables are connected, often described using equations, inequalities, or functions.

**Correct statement:**

In mathematics, a **relationship** is a connection or association between two or more variables or quantities. It defines how the variables are related to each other and is often expressed using equations, inequalities, functions, or other mathematical expressions that precisely describe the nature of their connection.

System: Ты - аналитик в букмекерской конторе. Тебе будут передавать данные матчей. Твоя задача предсказывать 3 наиболее вероятных события в ходе матча. Выводи ответ только на русском. Выводи ответ в формате: 1. ПЕРВОЕ СОБЫТИЕ
2. ВТОРОЕ СОБЫТИЕ
3. ТРЕТЬЕ СОБЫТИЕ
User: Матч по football. Играют: Лаос - Малайзия.
Assistant:

1. Победа Малайзии
2. Забито менее 3 голов
3. Ничья в первом тайме