690. Employee Importance

本文介绍了一种遍历员工信息结构的算法实现,通过递归查找特定ID及其下属的重要性值,实现对指定员工及其所有下属总重要性的计算。

** You are given a data structure of employee information, which includes the employee’s unique id, his importance value and his direct subordinates’ id.
For example, employee 1 is the leader of employee 2, and employee 2 is the leader of employee 3. They have importance value 15, 10 and 5, respectively. Then employee 1 has a data structure like [1, 15, [2]], and employee 2 has [2, 10, [3]], and employee 3 has [3, 5, []]. Note that although employee 3 is also a subordinate of employee 1, the relationship is not direct.
Now given the employee information of a company, and an employee id, you need to return the total importance value of this employee and all his subordinates.**

个人解题思路:通过遍历,寻找id对应的employees,记录其重要值importance,接着通过读取subordinates的值,进行迭代叠加。

/*
// Employee info
class Employee {
    // It's the unique id of each node;
    // unique id of this employee
    public int id;
    // the importance value of this employee
    public int importance;
    // the id of direct subordinates
    public List<Integer> subordinates;
};
*/
class Solution {
             public int getImportance(List<Employee> employees, int id) {
                 int sum = 0;
                 int result = getImportance(employees,id,sum);
                 return result;
                 }
             public int getImportance(List<Employee> employees, int id,int sum){
                 for(Employee e:employees){
                     if(e.id == id){
                         sum = sum + e.importance;
                         if(e.subordinates!=null)
                        for(Integer i:e.subordinates) sum = getImportance(employees,i,sum);     
                        break;
                     }

                 }
                 return sum;
             }
}

运行时间为23ms,通过参考给出的示例代码,发现自己忽略了employees的数据结构为list,没有利用充分利用这个数据结构,只把重心放在其数据类型Employee的构成。

### DIS QL Types in Database or Data Integration Systems Data Integration System Query Languages (DIS QLs) refer to specialized query languages designed specifically for handling complex queries across multiple heterogeneous databases within a unified framework. These systems aim to provide seamless access, transformation, and querying capabilities over disparate data sources. A key aspect of DIS QL is its ability to handle schema mappings, transformations, and federated queries effectively while maintaining performance and scalability[^1]. Below are some important characteristics: #### Characteristics of DIS QL - **Schema Mapping**: DIS QL supports defining relationships between schemas of different data sources through declarative mapping rules. - **Federated Queries**: It enables users to write single queries spanning multiple independent databases without needing detailed knowledge about their internal structures. - **Heterogeneity Handling**: Designed explicitly for environments where various types of structured/unstructured data coexist, such as relational tables, XML documents, JSON objects, etc., it provides mechanisms to unify these formats into one logical view[^2]. #### Example Code Snippet Demonstrating Schema Mapping Using SQL-like Syntax Here’s an example demonstrating how you might define simple schema mappings using a hypothetical DIS QL syntax inspired by standard SQL constructs: ```sql -- Define source-to-target mapping rule CREATE MAPPING EmployeeMapping AS ( SELECT e.id AS emp_id, e.name AS full_name, d.departmentName AS dept_name FROM SourceDB.EmployeeTable e JOIN TargetDB.DepartmentTable d ON e.deptId = d.id ); -- Execute cross-database query leveraging defined mapping SELECT * FROM EmployeeMapping WHERE dept_name LIKE 'Sales%'; ``` This snippet illustrates creating a reusable `EmployeeMapping` object which abstracts away complexities associated with joining two distinct yet related datasets residing on separate servers before executing further operations against this combined dataset representation. #### Importance in Modern Applications Modern applications increasingly rely upon integrating diverse information resources spread out globally via web services APIs, cloud storage solutions among others making robust support provided by advanced implementations crucial not only towards achieving desired functionality but also ensuring security compliance along privacy regulations like GDPR when dealing personally identifiable details contained therein[^3].
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值