JP2012059173A - Report preparation device and its program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To enhance automatization and improve efficiency in management of a graph generated from work data in order to reduce workload in creating a document and make a document that a user can easily use.SOLUTION: When report preparation means 10 generates a graph, report definition information acquisition means 13 acquires graph information from the report preparation means 10. Based on the acquired graph information, format condition reference means 14 and item extraction acquisition means 15 refer to a format from a graph title format dictionary 122 and retrieve a name composed of fixed character designation information, variable character designation information, or a combination of both information from an item dictionary 123. According to the referred format, graph title determination means 16 generates a graph title by combining the graph information item, a value extracted from a value of the item, the retrieved name, etc. Report naming means 17 gives the graph title to the generated graph.

Description

  The present invention relates to a technology for analyzing and processing a large amount of accumulated business data in a company and creating information handled by managers and employees as a report, and in particular, automatically naming and graphing a report. The present invention relates to a report generation apparatus to be classified and its program.

  Within a company, the counting management department processes business data (sales data, product shipment / inventory data, etc.) in the company's activities within the company, organizes it into a table, graphs, etc., and the results are sent to each department. To provide. As the providing method, various forms such as printing on a paper medium, attaching electronic data to an e-mail, and storing in a common document management server are used. In recent years, a large amount of business data (for example, sales data for each product or sales data for each store) is analyzed and processed in a database (DB), and is used for business decision making. Business Intelligence) tools are commercialized and sold, and by using such report creation tools, tables, graphs, etc. required by the counting management department in the company are created.

  In Patent Document 1, in a data object group automatic naming method for automatically naming a plurality of data object groups according to their contents, the contents, attributes or names of the respective data objects belonging to the data object group are used. A keyword is extracted, and the extracted keywords are displayed as data object group name candidates of the data object group or additional information candidates indicating the data object group. One keyword is selected from the displayed keywords, and the selected keyword is selected. A method of registering as a data object group name of the data object group or additional information indicating the data object group has been proposed.

JP 2000-231505 A

  When registering a graph created from business data, the title and classification of the category are done manually, so the business data to be processed has a different title name for the same graph. Different materials and data may be duplicated. In addition, processing data may need to be processed and analyzed, and processing and analysis may be leaked, and data and data provided to necessary departments in the company may be leaked. There is. In some cases, data, materials, etc. are provided to departments that do not need to be provided. Therefore, in order to prevent such a situation, the company's counting management department spends a considerable amount of time in the check work such as naming and category classification as a work separate from the work of creating the data. Currently.

  In addition, even for materials and data checked by the counting management department in this way, if the title name of the material is inappropriate, it is not necessary to misunderstand the title name of the material on the accepting side. It is often judged that it is present and discarded. In addition, since the management of the provided materials is the management of the receiving side, there was a problem that the materials to be viewed could not be retrieved immediately unless there was a regular maintenance and management manual mechanism.

  The graph is not disclosed or disclosed to the user, for example, at the discretion of the graph creator. Therefore, the graph creator may unintentionally publish a graph that should not be disclosed. For example, an undisclosed target graph is a graph that includes prediction data relating to a future business, or a graph that includes abnormal data that is clearly different from normal data due to some reason. This is because when these are disclosed, it may be disadvantageous for corporate activities or may cause erroneous behaviors to consumers. Such a graph is not appropriate for the graph creator to determine whether the graph creator is open / closed, but is not appropriate for the creation of these future forecast data or graphs containing outliers. The person in charge of management (positions) related to the management decision of the company should determine whether to make it public after creation.

  SUMMARY OF THE INVENTION In view of the above, the present invention aims to automate and improve the efficiency of management of graphs generated from business data in order to reduce the burden on the document creation side and make it easy for users to use. It is said.

  In order to solve the above-described problem, the present invention provides a report creation device for outputting or saving a graph generated from a database search result as a report, and for each generated graph, at least a graph type and a search for the database. A graph information storage means for storing graph information including a graph information item including a condition and a value of the item, a graph title naming for a combination of a graph type in the graph information item and a search condition for the database Storage means of a graph title format dictionary in which format information of a graph title consisting of fixed characters used in the above, variable character specification information specifying the graph information item, or a combination thereof, and stored in the graph information storage means Graph type and previous graph information A means for searching the graph title format dictionary by using a search condition for a database and determining format information of the corresponding graph title, and when the determined format information includes variable character designation information, from the graph information Means for reading a value of the graph information item indicated by the variable character designation information, and determining a graph title from a fixed character in the format information, a value of the graph information item, or a combination thereof.

  As a result, an appropriate graph title can be assigned uniformly and automatically to the generated graph, and business data can be analyzed and processed efficiently and in a short time.

  Further, the present invention is further characterized in that, in the above invention, a fixed character, a name designating a value acquisition target in the graph information, or a combination thereof is stored for the combination of the graph information item and the value of the item. And a means for determining the graph title, when the format information includes variable character designation information, the graph information item designated by the variable character designation information and the value of the item The item dictionary is searched for in combination, and if there is a combination of the corresponding graph information item and the value of the item in the item dictionary, the corresponding fixed character or the value acquisition target in the graph information is selected. The variable character part in the graph title is determined based on the specified name.

  As a result, for the graph title to be given to the generated graph, refer to the fixed character or the name that specifies the value acquisition target in the graph information, or a combination thereof from the item dictionary prepared in advance, depending on the graph information item. Therefore, a user-friendly name can be assigned.

  Further, the present invention is the above invention, further comprising a storage means storing a categorization condition using one or more graph information items as a classification condition, a graph information item used for the classification condition, and a value of the item. A category name dictionary storing means for storing fixed characters or variable character designation information for designating the graph information item for the combination, or a category name consisting of these combinations, and a graph stored in the graph information storing means Means for determining a registered category of graph information from the graph information item in the information according to each classification condition in the categorizing condition, and determining a category name for the registered category by referring to the category name dictionary .

  As a result, the category classification for managing the graph can be performed uniformly and automatically, so that it is possible to prevent the category of the graph registration from being unified by the graph creator. In particular, if there are many graphs to be created, the manual task of categorization is complicated, but it is easy to handle when viewing the graph because the categories are automatically classified to maintain uniformity. .

  Further, in the present invention, in the above invention, when the graph generated from the search result of the database is stored, the means for determining the graph title determines whether or not the same type of graph is already stored. Check the duplication by means of comparing graph titles, and if the same type of graph has already been saved, suppress the saving of a new graph or compare the period of data for which the graph is created , And means for overwriting and storing the other graph with the graph including the latest period.

  As a result, it is possible to prevent the same graph from being registered twice, thereby avoiding unnecessary data storage due to duplicate registration, thereby suppressing storage device pressure such as disk space due to unnecessary data. Can do.

  Further, in the present invention, in the above invention, when storing the graph generated from the search result of the database, means for determining whether or not the data of the graph includes a future predicted value, and a future prediction If the value is included, save the graph with an attribute that makes it non-public to anyone other than a specific authority, or make the graph public or private to a predetermined specific authority And a means for inquiring about this.

  As a result, among the generated graphs, the graph including the predicted value can be automatically set to be private. As a result, it can be made private so that it cannot be accessed by anyone other than a specific authority, and it can be made public only to users with a predetermined authority.

  Further, in the present invention, in the above invention, when storing the graph generated from the search result of the database, the means for determining whether or not the data of the graph includes an abnormal value according to a predetermined abnormal value determination method If an abnormal value is included, the graph is saved with an attribute that makes it non-public to anyone other than a person with specific authority, or it is made public or non-public to a person with a predetermined specific authority. And a means for inquiring whether to make it public.

  As a result, among the generated graphs, a graph including an abnormal value can be automatically set to be private. As a result, it can be made private so that only a person with specific authority can access it, and it can be made public only to users with a predetermined authority.

The effects of the present invention are as follows.
(1) The graph title can be automatically named by a method unified with the generated graph. As a result, even if the graph creators are different, the graph title is not given by the graph creator's subjectivity. Input mistakes when assigning the graph title, or different graph titles can be assigned to different graph titles. Registration as a graph can be avoided.
(2) Since category classification is performed based on the graph information, and the generated graph can be automatically associated and registered according to the category classification, a user who refers to the registered graph can perform category classification. It is possible to search and use the registered graph efficiently.
(3) The graph to be registered is checked to see if the same graph title as that of the already registered graph is registered. If it overlaps, registration of new graphs is suppressed (not recorded). Or, prioritize registration of graphs that reflect the latest data. As a result, it is possible to eliminate the registration of duplicate graphs, and it is possible to suppress the storage space pressure due to unnecessary data.
(4) Because graphs that include future predicted value data and abnormal values can be automatically set to non-public graphs, and other graphs can be registered as public settings. , It is possible to prevent accidental disclosure of a graph containing important information or information that causes confusion.

It is a figure which shows the apparatus structural example of embodiment of this invention. It is a process flowchart of the report preparation apparatus which concerns on embodiment of this invention. It is a process flowchart of the report preparation apparatus which concerns on embodiment of this invention. It is a figure which shows the example of a graph information table. It is a figure which shows the example of a graph title format dictionary. It is a figure which shows the example of an item dictionary. It is a processing flowchart of category classification. It is a figure explaining the classification conditions of categorization. It is a figure which shows the example of a category name dictionary and a categorization condition table. It is a processing flowchart of duplication check. It is a figure explaining a duplication check. It is a processing flowchart of non-public / public determination. It is a figure explaining the graph containing a predicted value. It is a figure explaining the method of determining the graph containing an abnormal value. It is a figure explaining the method of determining the graph containing an abnormal value. It is a figure which shows the example of a display of a registration graph. It is a figure which shows the example of a display of a registration graph.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. In the following, the function of the automatic naming means 11 in the report creating apparatus 1 will be mainly described.

  FIG. 1 shows an apparatus configuration example for explaining the outline of the present invention. In FIG. 1, reference numeral 1 is composed of a CPU and a storage device such as a hard disk and a memory. For example, reports such as graphs and tables are obtained from search results of a database (business DB) that stores business data such as sales data and sales data. A report creation device to be created, 2 is a business DB (database) in which business data is stored in advance, and 3 is an output device such as a display device, a printing device, or an external storage device.

  In the example of the apparatus configuration shown in FIG. 1, the user (graph creator) determines the DB search conditions such as the department, target period, and item to be searched for the data stored in the business DB 2, the format of the graph to be output, Is specified, and a report creation instruction is input to the report creation apparatus 1.

  Upon receiving this report creation instruction, the report creation means 10 of the report creation apparatus 1 uses the report definition information to search and extract data to be analyzed and processed from the business DB 2, and uses the report definition information based on the extracted data. Create the specified graph. Details of the graph creation processing by the report creation means 10 are well-known techniques, and thus the description thereof is omitted. The report service function provided by the report creation means 10 includes, for example, a relation creation database such as SQL, a report creation and distribution function installed in an application that handles other databases.

  The report definition information used by the report creation means 10 is, for example, an XML (Extensible Markup Language) file described in RDL (Report Definition Language). The RDL describes an instruction set that describes a graph layout, query information, and the like. RDL can perform data exchange between applications (for example, interoperability of commercial report products) by defining a common schema that enables report definition exchange as a communication method using reports.

  The automatic naming unit 11 sets information and attributes related to graph registration such as graph title assignment, category classification, duplication check, disclosure / non-disclosure, etc. for the graph generated by the report creation unit 10, and displays the set contents as a graph. It is saved in the storage means together with the graph to be registered and managed.

  For this purpose, the automatic naming means 11 is related to the format and the graph information storage unit 121 for storing the graph information acquired from the report definition information used by the report generating means 10, the graph title format dictionary 122 for storing the format related to the graph title, and the format. A condition storage unit 12 having an item dictionary 123 for storing items to be recorded, a report definition information acquisition unit 13 for acquiring graph information relating to the graph generated from the report generation unit 10, and a predetermined format for generating a graph title. Format condition reference means 14 to be referenced, item extraction acquisition means 15 for extracting necessary items from the condition storage means 12 based on the referenced format, graph title determination means 16 for determining a graph title from the extracted items, generation A report name that associates the determined graph with the determined graph title Means 17, category classification means 18 for classifying the category of the generated graph, duplication checking means 19 for checking whether or not the generated graph and the registered graph are duplicated, and registering the generated graph in a private attribute The private determination means 20 for determining whether or not, the registration storage means 21 for performing registration setting and storage of the generated graph, the report storage means 22 for storing the registered graph and related information, and the output device 3 Output means 23 for performing graph output is provided.

  2 and 3 are process flowcharts of the report creation apparatus 1 according to the embodiment of the present invention. The processing executed by the report creation device 1 will be specifically described with reference to FIGS.

  When a report creation instruction such as analysis or processing of data related to business data is input to the report creation means 10 of the report creation apparatus 1, this processing is started.

  Upon receiving the report creation instruction, the report creation means 10 acquires business data related to the report creation instruction from the business DB 2 according to the specified report definition information, and generates a graph using the acquired business data. After generating the graph, the report creation means 10 sends a graph generation (completion) notification to the automatic naming means 11.

  The report definition information acquisition means 13 of the automatic naming means 11 receives a graph generation notification from the report definition information used by the report creation means 10 (step S1).

  Next, the report definition information acquisition unit 13 acquires graph information about the generated graph from the report definition information used by the report generation unit 10 (step S2). The report definition information acquisition unit 13 stores a corresponding value in the graph information table 1211 for each item of the graph information acquired for the generated graph (step S3).

  Here, the graph information refers to the type of graph (graph type) related to the generated graph, the search condition (DB search condition) for searching data from the business DB 2 to generate the graph, and the business that acquired the data. DB2 table name (reference table name), vertical axis name of generated graph (vertical axis name), horizontal axis name (horizontal axis name), etc. (graph information item) and related information Information (value of the item). The graph information table 1211 is constructed in the graph information storage unit 121. For each generated graph, the graph information items including at least the graph type and the search condition for the business DB 2 for the graph information acquired from the report definition information, the values of the items, Is stored.

  FIG. 4 shows an example of the graph information table 1211. In the example illustrated in FIG. 4, the graph information table 1211 includes a graph type “bar graph”, a DB search condition “sales month = 200912, grouping by department code”, reference table name “sales table”, vertical axis name “sales”, A value is stored for each item such as the horizontal axis name “specified month”. Note that the DB search conditions shown in the graph information table 1211 in FIG. 4 are search conditions (corresponding to the WHERE clause of the SQL sentence) when data is searched from the business DB 2 as described in the supplementary explanation 1212, for example. The table name is a table name that refers to data in one or more tables provided in the business DB 2. The vertical axis name and horizontal axis name are the vertical axis name and horizontal axis name of the generated graph, and a graph (for example, a pie graph) that does not have them is the name of the corresponding part (fixed character). ).

  Next, the format condition reference means 14 determines the format of the graph title using the graph title format dictionary 122 (step S4). Here, the graph title format dictionary 122 includes at least fixed characters or graph information used for naming a graph title for a combination of a graph type used for a graph information item in the graph information table 1211 and a search condition for the business DB 2. Variable character designation information for designating items, or graph title format information (“format”) composed of a combination thereof is stored in advance.

  FIG. 5 shows an example of the graph title format dictionary 122. In the graph title format dictionary 122, as shown in FIG. 5, the “format” of the graph title is determined in advance according to the classification, type, and condition of the graph, and is stored in the condition storage unit 12. For example, as shown in FIG. 5A, the “classification” of the graph information is “graph type”, the “type” is “pie graph”, and the search condition of the DB search condition is “condition”. When there is a “sales table”, “format” = [search condition] + sales + ratio graph is defined. Therefore, in the graph title in this case, the portion that becomes the character corresponding to [search condition] is the variable character portion, and the graph title is composed of the variable character portion and the fixed characters of “sales” and “ratio graph”. Created by combination. Note that the “bar graph” in FIG. 5B is “format” = department + [vertical axis name] + transition graph, and [vertical axis name] is a variable character part. “Other” and “Transition graph” are fixed characters.

  Next, the item extraction / acquisition means 15 extracts variable character designation information of the determined “format” from the graph information and the item dictionary 123 as needed using the graph information table 1211 and the item dictionary 123 (step S5). . That is, the item extraction / acquisition means 15 determines the variable character portion characters in the “format” obtained by the format condition reference means 14 from the variable character designation information using the graph information table 1211 and the item dictionary 123.

  Here, the item dictionary 123 associates a name for each “value” with a “value” having a specified condition (key) as an “item” such as a DB search condition or a reference table name related to the business DB 2. . The item dictionary 123 is created in advance and stored in the condition storage unit 12.

  FIG. 6 shows an example of the item dictionary 123. As shown in FIG. 6, the items in the item dictionary 123 are DB search conditions and reference table names in the business DB 2, and the values that are designated conditions corresponding to the items are the search conditions “sales” in the DB search conditions. “Month” or “Group by department code”, “Sales table” as the table name, etc. As shown in FIG. 6, for each of these “values”, a fixed character such as “specified month”, “by department” or “by sales”, which is a numerical character (variable character part) of the corresponding month, is associated. .

  For example, as shown in FIG. 6, from the “search condition” in the “format” of the pie chart type of FIG. 5A, the value as the specified condition in the item DB search condition in the item dictionary 123 is “ “Sales month” is associated with “specified month” as variable character designation information. In this case, the item extraction / acquisition means 15 searches the graph information table 1211 for a variable character corresponding to “specified month” which is the variable character designation information, and extracts it. For example, numerical characters of sales month = 200912 are extracted.

  For example, from the variable character designation information of [search condition] in the “format” of the pie chart type of (a) shown in FIG. 5, in the item of the DB search condition in the item dictionary 123 as shown in FIG. When the value of the specified condition is “group by department code”, “by department” is associated as the name. In this case, the item extraction / acquisition means 15 sets the variable character designation information as a variable character portion as a fixed character of “by department” as variable character designation information. If not defined in the item dictionary 123, the value or name of the graph information is used.

  Next, when receiving the item used for the graph title from the item extraction / acquisition unit 15, the graph title determination unit 16 generates a graph title according to the above-described “format” (step S6). For example, in the case of “format” = [search condition] + sales + ratio graph, the graph title determination unit 16 sets “20092” as the variable character part related to [search condition] from the item extraction acquisition unit 15 and as a fixed character. The “sales” and “ratio graph” are received together with the information in the order of titles, and are arranged and generated as a graph title “December 2009 sales ratio graph”.

  Here, a case where a plurality of search conditions are related to the variable character portion [] will be described below by taking the “format” in FIG. 5 as an example. For example, the type of the generated graph is “pie chart”, and the values related to the DB search condition of the graph information item are “sales month” and “grouping by department code”, and the “format” is shown in FIG. Assume that (a) is selected. In the [search condition] relating to the variable character portion in the “format”, when the DB search condition is “sales month” and “grouping by department code”, the item extraction / acquisition means 15 includes the item dictionary 123 and the graph information table 1211. Are used to extract “200992” related to the “specified month” which is the variable character portion and “by department” which is the fixed character. When a plurality of variable character portions related to these [search conditions] are extracted from the item dictionary 123, the ranking of those fixed characters is previously set for each value in the item such as the DB search condition of the item dictionary 123. It is stored in the dictionary 123 (not shown). For example, in the above-described case, the name “by department” has a higher priority in the item dictionary 123 than “specified month”. As a result, the naming order in the graph title is determined as in “December 2009 by department” related to the variable character part. As a result, a graph title such as “December 2009 sales ratio graph” is generated by using the extracted variable character portions and the fixed characters “sales” and “ratio graph” in the format.

  Next, the report name assigning means 17 receives the graph generated from the report creating means 10 and the graph title assigned (determined) from the graph title determining means 16 and associates them (step S7). That is, the association between the generated graph and the graph information table 1211, the graph title format dictionary 122, and the item dictionary 123 stored in the condition storage unit 12 related to the assigned graph title is maintained. Used during processing.

  Next, the category classification unit 18 classifies the categories for the generated graph and the assigned graph title (step S8).

  FIG. 7 shows a detailed flow of step S8 shown in FIG. Hereinafter, steps S31 to S35 shown in FIG. 7 will be described.

  The category classification unit 18 acquires graph information stored in the graph information table 1211 for the generated graph (step S31).

  Next, the category classification means 18 sets classification conditions according to the categorization conditions according to the categorization condition table 182 (shown in FIG. 9B) from the acquired graph information (step S32). Here, the categorization condition means that the generated graph is classified into categories such as business departments that manage the graph, monthly sales, etc. (attributes according to purpose, target, etc.) and report storage means 22 is a condition for saving the data. In order to classify (categorize) categories, categorization conditions are determined in advance.

  FIG. 8 is a diagram for explaining a setting example of classification conditions used for categorization conditions. As shown in FIG. 8, as the classification condition setting item 180, for example, “search condition” for acquiring and classifying a search condition item from the DB search condition of graph information, and “graph type” for performing classification according to the graph type to be registered in the graph , There is a “reference table” that obtains a reference table from a DB search condition of graph information and classifies it by table. In addition, there is a “creation year / month” that is classified according to the year / month when the graph registration process is performed. In addition to this, for example, a “public department” that is classified according to the public department registered as master information of the graph registrant, Similarly, there are a “public target person” classified according to a public person, a “creator belonging department” classified according to a department, a “creator work type” classified according to a business, and the like.

  Next, the category classification means 18 refers to the category name dictionary 181 (shown in FIG. 9A) for each classification condition using the categorization condition table 182 (step S33). Subsequently, the category classification means 18 acquires a necessary category name or a part of the category name from the referenced category name dictionary 181 and generates a category name corresponding to the generated graph (step S34).

  Here, FIG. 9A shows an example of the category name dictionary 181 and FIG. 9B shows an example of the categorization condition table 182 and an example of the registered category 220 (220A). As shown in FIG. 9B, the categorization condition table 182 uses one or more graph information items for classifying the category of the generated graph as classification conditions. As the classification condition, for example, a search condition, a graph type, a reference table, a disclosure target department, a disclosure target person, and the like shown in the classification condition setting item 180 in FIG. 8 are used.

  The category name dictionary 181 stores variable character designation information for designating fixed characters or graph information items for combinations of graph information items used for classification conditions and the values of the items, or category names composed of combinations thereof. Is done. The category name dictionary 181 is prepared in advance and stored in a storage unit (not shown) of the category classification unit 18. In the category name dictionary 181, for example, category name = [department name] is associated with the search condition A of the classification condition (classification), and the category name = [[ Specified month name] is associated, category name = [item name] + ratio graph with classification condition graph type A, and similarly, category name = [vertical axis name] + transition graph with classification condition graph type B, etc. It is.

  Specifically, for example, as shown in FIG. 9, the category classification means 18 uses a categorization condition table 182A, category name = “department name” as classification condition # 1, and category name = [designated as classification condition # 2. The category name = [item name] + ratio graph, which is the graph type (pie graph), is referred to from the category name dictionary 181 as the classification name # 3. Further, the category classification means 18 uses the graph information table 1211 to extract variable characters corresponding to variable character designation information (parts designated by []) for designating these graph information items. As a result, the category classification means 18 generates a category name composed of fixed character or variable character designation information for designating a graph information item, or a combination thereof.

  Subsequently, the category classification means 18 classifies the category of the graph based on the generated category name (step S35). For example, as in the registration category 220A shown in FIG. 9B (“XXX department / designated month name / graph type”), the categories of the generated graph are classified and registered.

  In the categorization condition table 182 shown in FIG. 9B, any one of the setting items such as the search condition A, the search condition B, the graph type,... For each of the one or more classification conditions # 1 to #n. Is stored. These classification condition setting items 180 are preset in the categorization condition table 182. For the combination of classification conditions, the setter decides a predetermined rule and sets the categorization condition table 182 to reflect the determined rule. Thereby, the category classification means 18 can automatically perform the category classification of the generated graph according to the categorization conditions by the categorization condition table 182.

  For example, if the categorization condition table 182A shown in FIG. 9B includes classification conditions # 1 to # 3, categories are classified according to the categorization conditions including classification conditions # 1 to # 3, as shown as registered categories 220A in FIG. Will be classified. Further, there may be a categorization condition table 182B having a classification condition different from that of the categorization condition table 182A. The registration category 220B shown in FIG. 1 is a category generated based on the categorization condition table 182B, for example. In the example of FIG. 9B, it is assumed that classification is performed in the higher order of the category hierarchy in ascending order of numerical values such as classification condition # 1.

  One or a plurality of categorization condition tables 182 as described above can be selected by the category classification means 18 as the categorization condition table 182A or 182B, for example, depending on access conditions such as log information and ID information for each department of the graph creator. .

  When the processes in steps S31 to S35 are completed, the process in step S8 shown in FIG.

  Next, returning to the processing flow shown in FIG. 3, the duplication check means 19 classifies the assigned graph title into the categories 220A, 220B,... Of the report storage means 22 and stores other registered graphs. (Step S9). Since the report creation means 10 can easily generate a graph in response to a user's report creation instruction, the duplication check means 19 avoids registering the same graph redundantly. This duplication check may be executed immediately after step S6. In this case, duplication check with registered graphs in all categories is performed.

  FIG. 10 shows a detailed flow of step S9 shown in FIG. Hereinafter, steps S41 to S47 shown in FIG. 10 will be described.

  The duplication check means 19 acquires the generated graph and the determined graph title (step S41). Next, the duplication check unit 19 searches whether or not the same graph title exists in the same category of the report storage unit 22 (storage unit) based on the graph title (step S42).

  If there is no same graph title in step S42, the duplication checking means 19 sets graph registration (step S43). After setting, this duplication check process ends. On the other hand, when there is the same graph title in step S42, the duplication check means 19 compares the graph creation target period between the already registered graph and the generated graph (step S44).

  FIG. 11 is a diagram for explaining the duplication check. Specifically, in FIG. 11, an example of a method of comparing the registered graph 2201 stored in the report storage means 22 and the generated graph (registration check graph 1901) for the graph creation target period and checking for duplication. Show. It should be noted that the registration check graph 1901 and the registered graph 2201 have the same category classification [category] “XXX department / 2009 / sales transition category” (for example, the registered category 220A shown in FIG. 1), and the graph title is the same. [Title] It shall be the same in “XX Service Sales Trend Graph”. In addition, “/” in [Category] indicates the category classification for each category hierarchy, and “XX department”, “2009”, “sales transition category”, etc. are the category names of the category classification for each hierarchy. In this example, the left segment is the upper hierarchy. For example, in this case, the category “XX department” is the upper level of the category.

  As shown in FIG. 11, the registration check graph 1901 and the registered graph 2201 have the same [Category] and [Title], so the duplication check means 19 uses the registered graph 2201 and the registration check that have already been registered. The graph creation target period is compared with the graph 1901. Specifically, the duplication check means 19 refers to the graph information of the registration check graph 1901 and the graph information of the registered graph 2201, and the horizontal axis 1911 of the registration check graph 1901 and the horizontal axis 2211 of the registered graph 2201. Is determined to be the graph creation target period. In the duplication check means 19, the graph creation target period on the horizontal axis 1911 of the registration check graph 1901 is “January to April”, and the horizontal axis 2211 of the registered graph 2201 is “January to March”. It is determined that the registration check graph 1901 reflects the latest period. The acquired graph information is stored in the graph information table 1211 for each generated graph, but the graph information storage unit 121 records a predetermined period (for example, a history within one year from the latest registration date). ) Is left.

  Subsequently, when the graph generated in step S45 is not the latest period, the duplication check means 19 sets so as to suppress (not register) the graph registration of the generated graph (step S46). After setting, this duplication check process ends.

  On the other hand, if the graph generated in step S45 is the latest period, the duplication check means 19 sets the graph registration so that the generated graph is overwritten (step S47). For example, in the example of FIG. 11, the graph registration is set so that the registration check graph 1901 is overwritten on the registered graph 2201. After setting, this duplication check process ends. When the processes in steps S41 to S47 are finished, the process in step S9 shown in FIG. 3 is finished.

  In this example, the registered graph is overwritten with the graph including the latest period. However, if the graph title is the same, it is possible to suppress registration unconditionally or to inquire the graph creator for registration. is there.

  Next, returning to the processing flow shown in FIG. 3, the non-disclosure determination means 20 performs setting of non-disclosure / public (step S10).

For example, the nondisclosure determination means 20 performs the processing flow of step S10 by the following determination.
(1) Discrimination of graphs including future predicted values Compare the registered date of the graph with the latest date of registered graph data. If future (future) data is included, the predicted value is included. The graph is not disclosed.
(2) Discrimination of graphs including abnormal values (2-1) The same type of previous month created graph as the registered graph is displayed.
(2-2) Compare the scale (width of scale) between the registered graph and the previous month's graph.
(2-3) When the scales of the registered graph and the previous month created graph are larger (smaller) than specified, it is determined that the graph includes an abnormal value.
(2-4) In the case of a graph including an abnormal value, for example, a notification is sent to the administrator, and a determination is made as to whether it is public or private.

  FIG. 12 shows a detailed flow of step S10 shown in FIG. Hereinafter, steps S51 to S58 shown in FIG. 12 will be described.

  The private determination means 20 acquires the generated graph and the determined graph title (step S51). Subsequently, the non-disclosure determining means 20 acquires the latest date for the generated graph data (step S52).

  FIG. 13 is a diagram illustrating a graph including a predicted value. In the bar graph 2202 shown in FIG. 13, the date of registration is March 31, 2009, the latest date is 2009 (not shown), April to May, and the graph creation target period is 2009. The bar graphs from January to March are based on actual values, and the bar graphs from April to May 2009 are based on predicted values. In the case of the example shown in FIG. 13, in step S52, the non-disclosure determining means 20 obtains the latest date of sales, April-May 2009, which is “sales month” from the graph information of the created bar graph 2202. To do. As described above, for example, “sales month” = “200904, 200905” is acquired from the graph information table 1211.

  Next, the nondisclosure determination means 20 determines whether the acquired latest date includes future data as compared with the registered date (step S53). For example, in the case of the example shown in FIG. 13, if the registration date of the bar graph 2202 is March 31, 2009, the non-disclosure determining means 20 will perform actual data from January to March 2009 on the horizontal axis 2212. It is assumed that the period from April to May 2009 is a prediction period, and it is determined that the bar graph data on the vertical axis 2222 includes future data (predicted value data).

  If the acquired latest date does not contain future data, the process proceeds to step S55. On the other hand, if future data is included in the acquired latest date, the non-disclosure determining unit 20 sets the generated graph to non-disclosure (step S54). For example, in the case of the example shown in FIG. 13, the nondisclosure determination means 20 sets the bar graph 2202 to be nonpublic.

  Next, the non-disclosure determining means 20 searches for a previous month created graph or the like from registered graphs of the same type as the generated graph (step S55).

  For example, FIG. 14 and FIG. 15 are diagrams for explaining an abnormal value determination method. Note that the bar graph 2203 shown in FIG. 14 is a registered graph created in the previous month, the bar graph 2204 shown in FIG. 15 is a graph to be registered, and the bar graph 2203 and the bar graph 2204 are the same type of graph. In this case, the non-public determination means 20 searches for a registered graph (bar graph 2204) of the same type as the generated graph (bar graph 2203).

  Here, the same type of graph refers to a registered category 220 in which, for example, category names related to the category hierarchy match. The non-disclosure determining means 20 determines the determination conditions as to whether these graphs are the same type based on the classification conditions in the categorization condition table 182.

  For example, in the case of the categorization condition based on the categorization condition table 182 shown in FIG. 9B, the classification condition # 1 (search condition A) to the classification condition # 3 (graph type) are the same, and the classification condition # Those having different title names of “designated month names” 2 (search condition B) are set in advance as determination conditions (not shown) as graphs of the same type. 14 and 15 show categories of search conditions A “department name”, search condition B “designated month name”, and graph type B “bar graph” (for example, product composition ratio graph) shown in FIG. 9B, for example. The category names of the respective hierarchies in the classification match, and the specified month name (March 2009) of the bar graph 2203 and the specified month name (April 2009) of the bar graph 2204 are different, that is, the same type of graph.

  Next, the non-disclosure determining unit 20 compares the generated graph with the searched graph (step S56).

  Hereinafter, for example, the case where the non-disclosure determining unit 20 compares the bar graph 2203 (generated graph) shown in FIG. 14 with the bar graph 2204 (searched graph) shown in FIG. 15 will be described. In the following description, it is assumed that the graph generated by the report creation unit 10 and the registered graph are predetermined image data (for example, bitmap data). The image size of the bar graph 2203 shown in FIG. 14 is, for example, XP = 640 dots and YP = 480 dots, and the image size of the bar graph 2204 shown in FIG. 15 is the same. The image size of these graphs is defined in advance as a graph display area, and the vertex Point # 1, vertex Point # 2, etc. of the scale display area in the graph display area are also defined in advance.

  The non-disclosure determining means 20 obtains the image data in the frame of the vertex Point # 1 (25, 25) and vertex Point # 2 (125, 425) of the scale display area from the bar graph 2203 and the bar graph 2204, which are the same type of graph. , Get each. That is, the cutout image 2223 is acquired from the bar graph 2203 as shown in FIG. 14, and the cutout image 2224 is acquired from the bar graph 2204 as shown in FIG.

  The non-disclosure determining unit 20 acquires the dot number D # 1 of “color” other than the background color (for example, white) from the cutout image 2223. Similarly, the dot number D # 2 of “color” other than the background color is acquired from the cutout image 2224.

  When the numbers of dots D # 1 and D # 2 other than the background color are the same, the nondisclosure determination means 20 determines that the vertical scales of the bar graph 2203 and the bar graph 2204 are the same. When the number of dots D # 1 other than the background color is smaller than D # 2, the non-public determination means 20 determines that the scale of the vertical axis of the bar graph 2203 is larger (or smaller) than that of the bar graph 2204. When the number of dots D # 1 other than the background color is larger than D # 2, the non-disclosure determining unit 20 determines that the scale of the vertical axis of the bar graph 2203 is smaller (or larger) than that of the bar graph 2204.

  For example, since the number of dots D # 1 of the cutout image 2223 shown in FIG. 14 is larger than the number of dots D # 2 of the cutout image 2224 shown in FIG. 15, the non-disclosure determining unit 20 uses the bar graph 2204 (for example, scale 1000). Also, the bar graph 2203 (for example, scale 100) is determined to have a smaller vertical scale. That is, it is determined that the range of scale change is large.

  From the result of the comparison, the nondisclosure determination means 20 determines whether or not an abnormal value is included in the generated graph (step S57). Specifically, the non-disclosure determining unit 20 determines whether the graph data has undergone a rapid change, for example, by looking at the width of the scale change in the clipped image from the generated graph and the retrieved graph.

  For example, in the example shown in FIGS. 14 and 15, the number of dots D # 1 of the cutout image 2223 shown in FIG. 14 is larger than the number of dots D # 2 of the cutout image 2224 shown in FIG. 20, the bar graph 2203 (for example, the scale 100) determines that the scale of the vertical axis is smaller (or larger) than the bar graph 2204 (for example, the scale 1000). Note that in the non-disclosure determining means 20, an estimated value of the scale change width that the scale becomes relatively small or large as the number of dots increases or decreases is determined in advance and stored in a storage unit (not shown). The When the estimated value of the predetermined scale change width exceeds the predetermined change width reference, the non-public determination means 20 determines that an abnormal value is included in the generated graph.

  In the above description, an example of image data in which the registered graph is expressed in dots is shown, but graph data having a numerical value such as a scale such as a vertical axis or a horizontal axis, or bar graph data may be used. In this case, the non-public determination means 20 can easily acquire the numerical value and compare the scales between the graphs.

  If the graph generated in step S57 does not include an abnormal value, the nondisclosure determination means 20 assigns and registers an attribute so as to disclose the generated graph, and ends the secret determination processing. On the other hand, in the case where an abnormal value is included, the non-disclosure determination means 20 gives an attribute so as to make the generated graph private, and after the setting, the non-discrimination determination means 20 / The disclosure determination process is terminated (step S58). When the processes in steps S51 to S58 are completed, the process in step S10 shown in FIG. 3 is terminated.

  Note that, regarding the setting for giving an attribute so as to be private, the private decision means 20 may make a notification asking whether to set the graph generated via the output means 23 to private or public. good. Furthermore, in response to this notification, for example, the administrator makes the graph private to anyone other than a person with specific authority, or makes it public or private to a person with a predetermined specific authority. Until the setting is made, the private registration means 20 sets the graph registration of the graph to a pending or private attribute.

  Next, returning to the processing flow shown in FIG. 3, the registration storage unit 21 performs graph registration or registration suppression for the above setting contents (step S <b> 11). Specifically, when registering a graph for the generated graph, the registration storage unit 21 stores the generated graph in the report storage unit 22 in accordance with the category classification, and information on the category classification of the registered graph. Save graph registration information including attributes such as public or public. On the other hand, when the graph registration is not performed for the generated graph, the registration storage unit 21 does not store the generated graph in the report storage unit 22.

  As a result, the automatic naming unit 11 automatically names the graph title for the graph generated by the report creating unit 10 and registers or suppresses the graph in the report storage unit 22.

  16 and 17 show display examples of registered graphs. The screen display 31A shown in FIG. 16 shows the category classification of [Category] “XX department / month in 2009 /” shown in the category display 311A, and a bar graph 3121 (March degree in the list view display 312A corresponding to the classification). ), 3122 (April degree), 3123 (May degree), and a pie chart 3124 (May degree).

  The screen display 31B shown in FIG. 17 shows the category classification of [Category] “XX department / May 2009 /” shown in the category display 311B, and a bar graph 3123 (May degree) and a pie chart in the list view display 312B. 3124 (May degree) is displayed. 16 and 17, when the registered graph has a non-public attribute, it is not displayed in the list view tables 312A and 312B, for example.

  The output display as described above is a result of output to the output device 3 (for example, display device) based on the graph registration information stored (registered) in the report storage unit 22 by the output unit 23.

  The output unit 23 shows an example of an output interface for displaying and printing the graph registered in the report storage unit 22. In the configuration example shown in FIG. 1, the report creation device 1 is configured to include the report storage unit 22, but the report storage unit 22 may be included in another PC (server). In this case, for example, the server manages and restricts graph registration information, such as the public / private attributes of the registered graph, together with information such as the target person (department, job title, etc.), password, etc. You may do it. For example, the registered graph may be stored with a non-public attribute other than those having specific authority, or may be made public to a predetermined specific person.

  The processing performed by the above report creation apparatus can be realized by a computer and a software program, and the program can be recorded on a computer-readable recording medium or provided through a network.

  According to an embodiment of the present invention, a title name can be automatically and uniformly assigned to a graph generated by a report creation tool, and business data can be analyzed and processed efficiently and quickly. Can be done in time.

  In addition, according to one embodiment of the present invention, the category classification for managing the graph can be performed uniformly and automatically, thereby avoiding that the registered category is not unified by the graph creator. it can. In particular, if there are many graphs to be created, the manual task of categorization is complicated, but it is easy to handle when viewing the graph because the categories are automatically classified to maintain uniformity. .

  Also, according to one embodiment of the present invention, it is possible to prevent the same graph from being registered twice, thereby avoiding unnecessary data storage due to duplicate registration, and thereby disk space due to unnecessary data. It is possible to suppress the pressure on the storage device.

  In addition, according to an embodiment of the present invention, it is possible to automatically set a graph including a predicted value and a graph including an abnormal value to be private in the generated graph. As a result, it can be made private so that no one can access it, and it can be made public only to users with a predetermined authority.

1 Report creation device 2 Business DB (database)
DESCRIPTION OF SYMBOLS 3 Output device 10 Report preparation means 11 Automatic naming means 12 Condition storage means 13 Report definition information acquisition means 14 Format condition reference means 15 Item extraction acquisition means 16 Graph title determination means 17 Report name assignment means 18 Category classification means 19 Duplicate check means 20 Private determination means 21 Registration storage means 22 Report storage means 23 Output means 121 Graph information storage section 122 Graph title format dictionary 123 Item dictionary

Claims (7)

A report creation device that outputs or saves a graph generated from database search results as a report,
Graph information storage means for storing graph information including a graph information item including at least a graph type and a search condition for the database and a value of the item for each generated graph;
For the combination of the graph type in the graph information item and the search condition for the database, a fixed character used for naming the graph title, variable character designation information for designating the graph information item, or a graph title composed of a combination thereof Means for storing a graph title format dictionary storing format information of
Means for searching the graph title format dictionary using the graph type in the graph information stored in the graph information storage means and the search condition for the database, and determining the format information of the corresponding graph title;
When the determined format information includes variable character specification information, the value of the graph information item indicated by the variable character specification information is read from the graph information, and the fixed character in the format information or the value of the graph information item And a means for determining a graph title from the combination. The report creation device according to claim 1,
Furthermore, for a combination of the graph information item and the value of the item, a storage unit for storing a fixed character or a name for designating a value acquisition target in the graph information, or a combination thereof is stored.
The means for determining the graph title, when variable character designation information is included in the format information, determines the item dictionary by combining the graph information item designated by the variable character designation information and the value of the item. If there is a combination of the corresponding graph information item and the value of that item in the item dictionary, the graph is based on the corresponding fixed character or the name specifying the value acquisition target in the graph information. A report creation device characterized by determining a variable character part in a title. In the report creation device according to claim 1 or 2,
Storage means for storing categorization conditions using one or more graph information items as classification conditions;
For a combination of the graph information item used for the classification condition and the value of the item, a fixed character, variable character designation information for designating the graph information item, or a category name storing a category name composed of the combination Dictionary storage means;
From the graph information item in the graph information stored in the graph information storage means, determine a registered category of graph information according to each classification condition in the categorization condition, and refer to the category name dictionary to determine a category name for the registered category A report creation device comprising means for determining. In the report preparation apparatus of any one of Claim 1 to Claim 3,
A duplicate check means for comparing whether or not the same kind of graph is already saved when saving the graph generated from the search result of the database, by comparing the graph titles determined by the means for determining the graph title;
If a graph of the same type has already been saved, save the new graph, or compare the periods of the data for which the graph was created and compare the other graph with the graph containing the latest period. And a means for overwriting and storing the report creation device. In the report preparation apparatus of any one of Claim 1 to Claim 4,
Means for determining whether or not the data of the graph includes future predicted values when storing the graph generated from the search result of the database;
When including the predicted value in the future, save the graph with an attribute that makes it non-public to anyone other than those with specific authority, or make it public or non-existent to a person with certain predetermined authority And a means for inquiring whether to make the report public. In the report preparation apparatus of any one of Claim 1 to Claim 5,
Means for determining whether or not the data of the graph includes an abnormal value according to a predetermined abnormal value determination method when saving the graph generated from the search result of the database;
If an abnormal value is included, save the graph with an attribute that makes it non-public to anyone other than a specific authority, or make the graph public or private to a predetermined specific authority And a means for inquiring whether to do the report.   A report creation program for causing a computer to function as means included in the report creation apparatus according to any one of claims 1 to 6.

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